Note

This is one of my personal programming assignments after studying the course nlp sequence models at the 1st week and the copyright belongs to deeplearning.ai.

Improvise a Jazz Solo with an LSTM Network

Welcome to your final programming assignment of this week! In this notebook, you will implement a model that uses an LSTM to generate music. You will even be able to listen to your own music at the end of the assignment.

You will learn to:

Apply an LSTM to music generation.
Generate your own jazz music with deep learning.

Please run the following cell to load all the packages required in this assignment. This may take a few minutes.

from __future__ import print_function
import IPython
import sys
from music21 import *
import numpy as np
from grammar import *
from qa import *
from preprocess import * 
from music_utils import *
from data_utils import *
from keras.models import load_model, Model
from keras.layers import Dense, Activation, Dropout, Input, LSTM, Reshape, Lambda, RepeatVector
from keras.initializers import glorot_uniform
from keras.utils import to_categorical
from keras.optimizers import Adam
from keras import backend as K

Using TensorFlow backend.

1 - Problem statement

You would like to create a jazz music piece specially for a friend’s birthday. However, you don’t know any instruments or music composition. Fortunately, you know deep learning and will solve this problem using an LSTM netwok.

You will train a network to generate novel jazz solos in a style representative of a body of performed work.

1.1 - Dataset

You will train your algorithm on a corpus of Jazz music. Run the cell below to listen to a snippet of the audio from the training set:

1	IPython.display.Audio('./data/30s_seq.mp3')

            <audio controls="controls" >
                <source src="http://rufzo5fy8.hn-bkt.clouddn.com/gitpage/deeplearning.ai/nlp-sequence-models/jupter/week1/Jazz_improvisation_with_LSTM/data/30s_seq.mp3" type="audio/mpeg" />
                Your browser does not support the audio element.
            </audio>

We have taken care of the preprocessing of the musical data to render it in terms of musical “values.” You can informally think of each “value” as a note, which comprises a pitch and a duration. For example, if you press down a specific piano key for 0.5 seconds, then you have just played a note. In music theory, a “value” is actually more complicated than this–specifically, it also captures the information needed to play multiple notes at the same time. For example, when playing a music piece, you might press down two piano keys at the same time (playng multiple notes at the same time generates what’s called a “chord”). But we don’t need to worry about the details of music theory for this assignment. For the purpose of this assignment, all you need to know is that we will obtain a dataset of values, and will learn an RNN model to generate sequences of values.

Our music generation system will use 78 unique values. Run the following code to load the raw music data and preprocess it into values. This might take a few minutes.

X, Y, n_values, indices_values = load_music_utils()
print('shape of X:', X.shape)
print('number of training examples:', X.shape[0])
print('Tx (length of sequence):', X.shape[1])
print('total # of unique values:', n_values)
print('Shape of Y:', Y.shape)

shape of X: (60, 30, 78)
number of training examples: 60
Tx (length of sequence): 30
total # of unique values: 78
Shape of Y: (30, 60, 78)

You have just loaded the following:

X: This is an (m, $T_x$, 78) dimensional array. We have m training examples, each of which is a snippet of $T_x =30$ musical values. At each time step, the input is one of 78 different possible values, represented as a one-hot vector. Thus for example, X[i,t,:] is a one-hot vector representating the value of the i-th example at time t.
Y: This is essentially the same as X, but shifted one step to the left (to the past). Similar to the dinosaurus assignment, we’re interested in the network using the previous values to predict the next value, so our sequence model will try to predict $y^{\langle t \rangle}$ given $x^{\langle 1\rangle}, \ldots, x^{\langle t \rangle}$. However, the data in Y is reordered to be dimension $(T_y, m, 78)$, where $T_y = T_x$. This format makes it more convenient to feed to the LSTM later.
n_values: The number of unique values in this dataset. This should be 78.
indices_values: python dictionary mapping from 0-77 to musical values.

1.2 - Overview of our model

Here is the architecture of the model we will use. This is similar to the Dinosaurus model you had used in the previous notebook, except that in you will be implementing it in Keras. The architecture is as follows:

We will be training the model on random snippets of 30 values taken from a much longer piece of music. Thus, we won’t bother to set the first input $x^{\langle 1 \rangle} = \vec{0}$, which we had done previously to denote the start of a dinosaur name, since now most of these snippets of audio start somewhere in the middle of a piece of music. We are setting each of the snippts to have the same length $T_x = 30$ to make vectorization easier.

2 - Building the model

In this part you will build and train a model that will learn musical patterns. To do so, you will need to build a model that takes in X of shape $(m, T_x, 78)$ and Y of shape $(T_y, m, 78)$. We will use an LSTM with 64 dimensional hidden states. Lets set n_a = 64.

n_a = 64

Here’s how you can create a Keras model with multiple inputs and outputs. If you’re building an RNN where even at test time entire input sequence $x^{\langle 1 \rangle}, x^{\langle 2 \rangle}, \ldots, x^{\langle T_x \rangle}$ were given in advance, for example if the inputs were words and the output was a label, then Keras has simple built-in functions to build the model. However, for sequence generation, at test time we don’t know all the values of $x^{\langle t\rangle}$ in advance; instead we generate them one at a time using $x^{\langle t\rangle} = y^{\langle t-1 \rangle}$. So the code will be a bit more complicated, and you’ll need to implement your own for-loop to iterate over the different time steps.

The function djmodel() will call the LSTM layer $T_x$ times using a for-loop, and it is important that all $T_x$ copies have the same weights. I.e., it should not re-initiaiize the weights every time—the $T_x$ steps should have shared weights. The key steps for implementing layers with shareable weights in Keras are:

Define the layer objects (we will use global variables for this).
Call these objects when propagating the input.

We have defined the layers objects you need as global variables. Please run the next cell to create them. Please check the Keras documentation to make sure you understand what these layers are: Reshape(), LSTM(), Dense().

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reshapor = Reshape((1, 78))                        # Used in Step 2.B of djmodel(), below
LSTM_cell = LSTM(n_a, return_state = True)         # Used in Step 2.C
densor = Dense(n_values, activation='softmax')     # Used in Step 2.D

Each of reshapor, LSTM_cell and densor are now layer objects, and you can use them to implement djmodel(). In order to propagate a Keras tensor object X through one of these layers, use layer_object(X) (or layer_object([X,Y]) if it requires multiple inputs.). For example, reshapor(X) will propagate X through the Reshape((1,78)) layer defined above.

Exercise: Implement djmodel(). You will need to carry out 2 steps:

Create an empty list “outputs” to save the outputs of the LSTM Cell at every time step.

Loop for $t \in 1, \ldots, T_x$:

A. Select the “t”th time-step vector from X. The shape of this selection should be (78,). To do so, create a custom Lambda layer in Keras by using this line of code:

           x = Lambda(lambda x: X[:,t,:])(X)
``` 
Look over the Keras documentation to figure out what this does. It is creating a "temporary" or "unnamed" function (that's what Lambda functions are) that extracts out the appropriate one-hot vector, and making this function a Keras `Layer` object to apply to `X`. 

    B. Reshape x to be (1,78). You may find the `reshapor()` layer (defined below) helpful.

    C. Run x through one step of LSTM_cell. Remember to initialize the LSTM_cell with the previous step's hidden state $a$ and cell state $c$. Use the following formatting:
```python
a, _, c = LSTM_cell(input_x, initial_state=[previous hidden state, previous cell state])

D. Propagate the LSTM’s output activation value through a dense+softmax layer using densor.

E. Append the predicted value to the list of “outputs”

# GRADED FUNCTION: djmodel

def djmodel(Tx, n_a, n_values):
    """
    Implement the model
    
    Arguments:
    Tx -- length of the sequence in a corpus
    n_a -- the number of activations used in our model
    n_values -- number of unique values in the music data 
    
    Returns:
    model -- a keras model with the 
    """
    
    # Define the input of your model with a shape 
    X = Input(shape=(Tx, n_values))
    
    # Define s0, initial hidden state for the decoder LSTM
    a0 = Input(shape=(n_a,), name='a0')
    c0 = Input(shape=(n_a,), name='c0')
    a = a0
    c = c0
    
    ### START CODE HERE ### 
    # Step 1: Create empty list to append the outputs while you iterate (≈1 line)
    outputs = [];
    
    # Step 2: Loop
    for t in range(Tx):
        
        # Step 2.A: select the "t"th time step vector from X. 
        x = Lambda(lambda x: X[:,t,:])(X);
        # Step 2.B: Use reshapor to reshape x to be (1, n_values) (≈1 line)
        x = reshapor(x);
        # Step 2.C: Perform one step of the LSTM_cell
        a, _, c = LSTM_cell(x, initial_state=[a, c]);
        # Step 2.D: Apply densor to the hidden state output of LSTM_Cell
        out = densor(a);
        # Step 2.E: add the output to "outputs"
        p = outputs.append(out);
        
    # Step 3: Create model instance
    model = Model(input=[X, a0, c0], outputs = outputs);
    
    ### END CODE HERE ###
    
    return model

Run the following cell to define your model. We will use Tx=30, n_a=64 (the dimension of the LSTM activations), and n_values=78. This cell may take a few seconds to run.

1	model = djmodel(Tx = 30 , n_a = 64, n_values = 78)

/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:44: UserWarning: Update your `Model` call to the Keras 2 API: `Model(outputs=[<tf.Tenso..., inputs=[<tf.Tenso...)`

You now need to compile your model to be trained. We will Adam and a categorical cross-entropy loss.

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opt = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, decay=0.01)

model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])

Finally, lets initialize a0 and c0 for the LSTM’s initial state to be zero.

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m = 60
a0 = np.zeros((m, n_a))
c0 = np.zeros((m, n_a))

Lets now fit the model! We will turn Y to a list before doing so, since the cost function expects Y to be provided in this format (one list item per time-step). So list(Y) is a list with 30 items, where each of the list items is of shape (60,78). Lets train for 100 epochs. This will take a few minutes.

1	model.fit([X, a0, c0], list(Y), epochs=100)

Epoch 1/100
60/60 [==============================] - 5s - loss: 125.8264 - dense_1_loss_1: 4.3545 - dense_1_loss_2: 4.3464 - dense_1_loss_3: 4.3425 - dense_1_loss_4: 4.3442 - dense_1_loss_5: 4.3421 - dense_1_loss_6: 4.3446 - dense_1_loss_7: 4.3401 - dense_1_loss_8: 4.3457 - dense_1_loss_9: 4.3314 - dense_1_loss_10: 4.3323 - dense_1_loss_11: 4.3423 - dense_1_loss_12: 4.3389 - dense_1_loss_13: 4.3364 - dense_1_loss_14: 4.3380 - dense_1_loss_15: 4.3371 - dense_1_loss_16: 4.3311 - dense_1_loss_17: 4.3417 - dense_1_loss_18: 4.3396 - dense_1_loss_19: 4.3346 - dense_1_loss_20: 4.3342 - dense_1_loss_21: 4.3366 - dense_1_loss_22: 4.3406 - dense_1_loss_23: 4.3338 - dense_1_loss_24: 4.3317 - dense_1_loss_25: 4.3376 - dense_1_loss_26: 4.3340 - dense_1_loss_27: 4.3329 - dense_1_loss_28: 4.3416 - dense_1_loss_29: 4.3399 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0000e+00 - dense_1_acc_2: 0.0500 - dense_1_acc_3: 0.0500 - dense_1_acc_4: 0.0167 - dense_1_acc_5: 0.0500 - dense_1_acc_6: 0.0333 - dense_1_acc_7: 0.0500 - dense_1_acc_8: 0.0000e+00 - dense_1_acc_9: 0.1000 - dense_1_acc_10: 0.0333 - dense_1_acc_11: 0.0167 - dense_1_acc_12: 0.0667 - dense_1_acc_13: 0.0500 - dense_1_acc_14: 0.0667 - dense_1_acc_15: 0.0667 - dense_1_acc_16: 0.0500 - dense_1_acc_17: 0.0500 - dense_1_acc_18: 0.0167 - dense_1_acc_19: 0.1000 - dense_1_acc_20: 0.0667 - dense_1_acc_21: 0.0500 - dense_1_acc_22: 0.0667 - dense_1_acc_23: 0.1167 - dense_1_acc_24: 0.1000 - dense_1_acc_25: 0.0333 - dense_1_acc_26: 0.1000 - dense_1_acc_27: 0.0500 - dense_1_acc_28: 0.0500 - dense_1_acc_29: 0.0833 - dense_1_acc_30: 0.0000e+00                                                                 
Epoch 2/100
60/60 [==============================] - 0s - loss: 122.6142 - dense_1_loss_1: 4.3317 - dense_1_loss_2: 4.2991 - dense_1_loss_3: 4.2729 - dense_1_loss_4: 4.2763 - dense_1_loss_5: 4.2523 - dense_1_loss_6: 4.2653 - dense_1_loss_7: 4.2464 - dense_1_loss_8: 4.2352 - dense_1_loss_9: 4.2288 - dense_1_loss_10: 4.2197 - dense_1_loss_11: 4.2248 - dense_1_loss_12: 4.2489 - dense_1_loss_13: 4.2078 - dense_1_loss_14: 4.2074 - dense_1_loss_15: 4.2073 - dense_1_loss_16: 4.1991 - dense_1_loss_17: 4.2009 - dense_1_loss_18: 4.2387 - dense_1_loss_19: 4.1921 - dense_1_loss_20: 4.2132 - dense_1_loss_21: 4.2112 - dense_1_loss_22: 4.1933 - dense_1_loss_23: 4.1941 - dense_1_loss_24: 4.2164 - dense_1_loss_25: 4.2240 - dense_1_loss_26: 4.1728 - dense_1_loss_27: 4.2027 - dense_1_loss_28: 4.2063 - dense_1_loss_29: 4.2258 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1333 - dense_1_acc_3: 0.1500 - dense_1_acc_4: 0.1667 - dense_1_acc_5: 0.2000 - dense_1_acc_6: 0.1167 - dense_1_acc_7: 0.1667 - dense_1_acc_8: 0.1167 - dense_1_acc_9: 0.1833 - dense_1_acc_10: 0.1667 - dense_1_acc_11: 0.2000 - dense_1_acc_12: 0.0667 - dense_1_acc_13: 0.1333 - dense_1_acc_14: 0.1333 - dense_1_acc_15: 0.1167 - dense_1_acc_16: 0.1833 - dense_1_acc_17: 0.2000 - dense_1_acc_18: 0.0667 - dense_1_acc_19: 0.1333 - dense_1_acc_20: 0.1667 - dense_1_acc_21: 0.1333 - dense_1_acc_22: 0.1000 - dense_1_acc_23: 0.1167 - dense_1_acc_24: 0.1333 - dense_1_acc_25: 0.1167 - dense_1_acc_26: 0.1833 - dense_1_acc_27: 0.1000 - dense_1_acc_28: 0.1833 - dense_1_acc_29: 0.0833 - dense_1_acc_30: 0.0000e+00     
Epoch 3/100
60/60 [==============================] - 0s - loss: 116.8061 - dense_1_loss_1: 4.3093 - dense_1_loss_2: 4.2449 - dense_1_loss_3: 4.1836 - dense_1_loss_4: 4.1745 - dense_1_loss_5: 4.1156 - dense_1_loss_6: 4.1481 - dense_1_loss_7: 4.0958 - dense_1_loss_8: 4.0446 - dense_1_loss_9: 3.9897 - dense_1_loss_10: 3.8988 - dense_1_loss_11: 3.8989 - dense_1_loss_12: 4.1165 - dense_1_loss_13: 3.8994 - dense_1_loss_14: 3.8898 - dense_1_loss_15: 3.9828 - dense_1_loss_16: 3.9182 - dense_1_loss_17: 3.8867 - dense_1_loss_18: 4.2104 - dense_1_loss_19: 3.8670 - dense_1_loss_20: 4.0711 - dense_1_loss_21: 4.0630 - dense_1_loss_22: 3.9217 - dense_1_loss_23: 3.9589 - dense_1_loss_24: 4.0469 - dense_1_loss_25: 4.0823 - dense_1_loss_26: 3.7266 - dense_1_loss_27: 3.9689 - dense_1_loss_28: 3.9623 - dense_1_loss_29: 4.1299 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1500 - dense_1_acc_3: 0.2000 - dense_1_acc_4: 0.1333 - dense_1_acc_5: 0.1833 - dense_1_acc_6: 0.1000 - dense_1_acc_7: 0.1167 - dense_1_acc_8: 0.0833 - dense_1_acc_9: 0.1167 - dense_1_acc_10: 0.1167 - dense_1_acc_11: 0.0833 - dense_1_acc_12: 0.0167 - dense_1_acc_13: 0.1000 - dense_1_acc_14: 0.1000 - dense_1_acc_15: 0.0500 - dense_1_acc_16: 0.0833 - dense_1_acc_17: 0.1000 - dense_1_acc_18: 0.0167 - dense_1_acc_19: 0.1000 - dense_1_acc_20: 0.0667 - dense_1_acc_21: 0.0667 - dense_1_acc_22: 0.0500 - dense_1_acc_23: 0.0833 - dense_1_acc_24: 0.0833 - dense_1_acc_25: 0.0167 - dense_1_acc_26: 0.1167 - dense_1_acc_27: 0.0500 - dense_1_acc_28: 0.0667 - dense_1_acc_29: 0.0333 - dense_1_acc_30: 0.0000e+00             
Epoch 4/100
60/60 [==============================] - 0s - loss: 112.2963 - dense_1_loss_1: 4.2889 - dense_1_loss_2: 4.1981 - dense_1_loss_3: 4.0962 - dense_1_loss_4: 4.0810 - dense_1_loss_5: 3.9790 - dense_1_loss_6: 4.0129 - dense_1_loss_7: 3.9439 - dense_1_loss_8: 3.7697 - dense_1_loss_9: 3.8046 - dense_1_loss_10: 3.6386 - dense_1_loss_11: 3.7236 - dense_1_loss_12: 3.9783 - dense_1_loss_13: 3.7060 - dense_1_loss_14: 3.7075 - dense_1_loss_15: 3.7358 - dense_1_loss_16: 3.7286 - dense_1_loss_17: 3.8079 - dense_1_loss_18: 3.9018 - dense_1_loss_19: 3.6729 - dense_1_loss_20: 3.9865 - dense_1_loss_21: 3.9529 - dense_1_loss_22: 3.8378 - dense_1_loss_23: 3.7695 - dense_1_loss_24: 3.7576 - dense_1_loss_25: 3.9597 - dense_1_loss_26: 3.6666 - dense_1_loss_27: 3.6978 - dense_1_loss_28: 3.8733 - dense_1_loss_29: 4.0193 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1500 - dense_1_acc_3: 0.2167 - dense_1_acc_4: 0.1833 - dense_1_acc_5: 0.2667 - dense_1_acc_6: 0.1333 - dense_1_acc_7: 0.1667 - dense_1_acc_8: 0.1833 - dense_1_acc_9: 0.1667 - dense_1_acc_10: 0.1667 - dense_1_acc_11: 0.1667 - dense_1_acc_12: 0.1000 - dense_1_acc_13: 0.1500 - dense_1_acc_14: 0.2167 - dense_1_acc_15: 0.1000 - dense_1_acc_16: 0.1167 - dense_1_acc_17: 0.1000 - dense_1_acc_18: 0.1000 - dense_1_acc_19: 0.1500 - dense_1_acc_20: 0.0833 - dense_1_acc_21: 0.0667 - dense_1_acc_22: 0.1167 - dense_1_acc_23: 0.0833 - dense_1_acc_24: 0.0000e+00 - dense_1_acc_25: 0.1000 - dense_1_acc_26: 0.1000 - dense_1_acc_27: 0.0833 - dense_1_acc_28: 0.1167 - dense_1_acc_29: 0.0667 - dense_1_acc_30: 0.0000e+00     
Epoch 5/100
60/60 [==============================] - 0s - loss: 110.0390 - dense_1_loss_1: 4.2729 - dense_1_loss_2: 4.1581 - dense_1_loss_3: 4.0292 - dense_1_loss_4: 4.0164 - dense_1_loss_5: 3.8981 - dense_1_loss_6: 3.9318 - dense_1_loss_7: 3.8775 - dense_1_loss_8: 3.6710 - dense_1_loss_9: 3.7225 - dense_1_loss_10: 3.5653 - dense_1_loss_11: 3.6287 - dense_1_loss_12: 3.8595 - dense_1_loss_13: 3.6459 - dense_1_loss_14: 3.6176 - dense_1_loss_15: 3.7001 - dense_1_loss_16: 3.6384 - dense_1_loss_17: 3.7419 - dense_1_loss_18: 3.7274 - dense_1_loss_19: 3.6644 - dense_1_loss_20: 3.8134 - dense_1_loss_21: 3.8085 - dense_1_loss_22: 3.7113 - dense_1_loss_23: 3.6167 - dense_1_loss_24: 3.6441 - dense_1_loss_25: 3.9445 - dense_1_loss_26: 3.7134 - dense_1_loss_27: 3.6405 - dense_1_loss_28: 3.8265 - dense_1_loss_29: 3.9533 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0333 - dense_1_acc_2: 0.1333 - dense_1_acc_3: 0.2167 - dense_1_acc_4: 0.2000 - dense_1_acc_5: 0.1333 - dense_1_acc_6: 0.0333 - dense_1_acc_7: 0.1000 - dense_1_acc_8: 0.1500 - dense_1_acc_9: 0.0833 - dense_1_acc_10: 0.1000 - dense_1_acc_11: 0.1000 - dense_1_acc_12: 0.0667 - dense_1_acc_13: 0.1000 - dense_1_acc_14: 0.1500 - dense_1_acc_15: 0.0833 - dense_1_acc_16: 0.0500 - dense_1_acc_17: 0.0500 - dense_1_acc_18: 0.0833 - dense_1_acc_19: 0.0333 - dense_1_acc_20: 0.0500 - dense_1_acc_21: 0.0833 - dense_1_acc_22: 0.0833 - dense_1_acc_23: 0.1667 - dense_1_acc_24: 0.0500 - dense_1_acc_25: 0.0500 - dense_1_acc_26: 0.0500 - dense_1_acc_27: 0.0833 - dense_1_acc_28: 0.0167 - dense_1_acc_29: 0.0167 - dense_1_acc_30: 0.0000e+00     
Epoch 6/100
60/60 [==============================] - 0s - loss: 106.1460 - dense_1_loss_1: 4.2571 - dense_1_loss_2: 4.1230 - dense_1_loss_3: 3.9604 - dense_1_loss_4: 3.9405 - dense_1_loss_5: 3.8132 - dense_1_loss_6: 3.8401 - dense_1_loss_7: 3.7750 - dense_1_loss_8: 3.5455 - dense_1_loss_9: 3.5752 - dense_1_loss_10: 3.4639 - dense_1_loss_11: 3.5982 - dense_1_loss_12: 3.7733 - dense_1_loss_13: 3.5049 - dense_1_loss_14: 3.4641 - dense_1_loss_15: 3.5221 - dense_1_loss_16: 3.5189 - dense_1_loss_17: 3.5414 - dense_1_loss_18: 3.5307 - dense_1_loss_19: 3.5341 - dense_1_loss_20: 3.6316 - dense_1_loss_21: 3.6324 - dense_1_loss_22: 3.5577 - dense_1_loss_23: 3.5073 - dense_1_loss_24: 3.5296 - dense_1_loss_25: 3.8212 - dense_1_loss_26: 3.4278 - dense_1_loss_27: 3.4614 - dense_1_loss_28: 3.5999 - dense_1_loss_29: 3.6956 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1667 - dense_1_acc_3: 0.2000 - dense_1_acc_4: 0.2000 - dense_1_acc_5: 0.2500 - dense_1_acc_6: 0.0833 - dense_1_acc_7: 0.0833 - dense_1_acc_8: 0.1667 - dense_1_acc_9: 0.1000 - dense_1_acc_10: 0.2000 - dense_1_acc_11: 0.1333 - dense_1_acc_12: 0.1000 - dense_1_acc_13: 0.1833 - dense_1_acc_14: 0.2167 - dense_1_acc_15: 0.1167 - dense_1_acc_16: 0.1167 - dense_1_acc_17: 0.1333 - dense_1_acc_18: 0.1667 - dense_1_acc_19: 0.1833 - dense_1_acc_20: 0.1167 - dense_1_acc_21: 0.1500 - dense_1_acc_22: 0.1500 - dense_1_acc_23: 0.1833 - dense_1_acc_24: 0.1167 - dense_1_acc_25: 0.0667 - dense_1_acc_26: 0.2000 - dense_1_acc_27: 0.1000 - dense_1_acc_28: 0.1500 - dense_1_acc_29: 0.0667 - dense_1_acc_30: 0.0000e+00     
Epoch 7/100
60/60 [==============================] - 0s - loss: 102.2579 - dense_1_loss_1: 4.2413 - dense_1_loss_2: 4.0875 - dense_1_loss_3: 3.8934 - dense_1_loss_4: 3.8654 - dense_1_loss_5: 3.7056 - dense_1_loss_6: 3.7368 - dense_1_loss_7: 3.6732 - dense_1_loss_8: 3.4290 - dense_1_loss_9: 3.4259 - dense_1_loss_10: 3.3381 - dense_1_loss_11: 3.4889 - dense_1_loss_12: 3.6443 - dense_1_loss_13: 3.3488 - dense_1_loss_14: 3.3007 - dense_1_loss_15: 3.3981 - dense_1_loss_16: 3.3846 - dense_1_loss_17: 3.3449 - dense_1_loss_18: 3.3858 - dense_1_loss_19: 3.4057 - dense_1_loss_20: 3.4521 - dense_1_loss_21: 3.4389 - dense_1_loss_22: 3.3936 - dense_1_loss_23: 3.4140 - dense_1_loss_24: 3.3620 - dense_1_loss_25: 3.6902 - dense_1_loss_26: 3.2316 - dense_1_loss_27: 3.3343 - dense_1_loss_28: 3.3640 - dense_1_loss_29: 3.4791 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1333 - dense_1_acc_3: 0.2167 - dense_1_acc_4: 0.1833 - dense_1_acc_5: 0.2667 - dense_1_acc_6: 0.1667 - dense_1_acc_7: 0.1333 - dense_1_acc_8: 0.2333 - dense_1_acc_9: 0.1667 - dense_1_acc_10: 0.2000 - dense_1_acc_11: 0.1833 - dense_1_acc_12: 0.1333 - dense_1_acc_13: 0.1667 - dense_1_acc_14: 0.2667 - dense_1_acc_15: 0.1667 - dense_1_acc_16: 0.1500 - dense_1_acc_17: 0.1833 - dense_1_acc_18: 0.1167 - dense_1_acc_19: 0.1333 - dense_1_acc_20: 0.1833 - dense_1_acc_21: 0.1167 - dense_1_acc_22: 0.1333 - dense_1_acc_23: 0.1333 - dense_1_acc_24: 0.1500 - dense_1_acc_25: 0.0667 - dense_1_acc_26: 0.2167 - dense_1_acc_27: 0.1000 - dense_1_acc_28: 0.1500 - dense_1_acc_29: 0.1833 - dense_1_acc_30: 0.0000e+00     
Epoch 8/100
60/60 [==============================] - 0s - loss: 98.0187 - dense_1_loss_1: 4.2277 - dense_1_loss_2: 4.0477 - dense_1_loss_3: 3.8258 - dense_1_loss_4: 3.7791 - dense_1_loss_5: 3.6089 - dense_1_loss_6: 3.6218 - dense_1_loss_7: 3.5396 - dense_1_loss_8: 3.2991 - dense_1_loss_9: 3.2584 - dense_1_loss_10: 3.1349 - dense_1_loss_11: 3.2992 - dense_1_loss_12: 3.4534 - dense_1_loss_13: 3.1133 - dense_1_loss_14: 3.0906 - dense_1_loss_15: 3.2273 - dense_1_loss_16: 3.2308 - dense_1_loss_17: 3.1062 - dense_1_loss_18: 3.2503 - dense_1_loss_19: 3.2314 - dense_1_loss_20: 3.2470 - dense_1_loss_21: 3.2910 - dense_1_loss_22: 3.2553 - dense_1_loss_23: 3.2761 - dense_1_loss_24: 3.2245 - dense_1_loss_25: 3.5042 - dense_1_loss_26: 3.0631 - dense_1_loss_27: 3.2291 - dense_1_loss_28: 3.2519 - dense_1_loss_29: 3.3307 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1167 - dense_1_acc_3: 0.1667 - dense_1_acc_4: 0.1667 - dense_1_acc_5: 0.2667 - dense_1_acc_6: 0.1833 - dense_1_acc_7: 0.1500 - dense_1_acc_8: 0.2667 - dense_1_acc_9: 0.1667 - dense_1_acc_10: 0.2333 - dense_1_acc_11: 0.1667 - dense_1_acc_12: 0.1167 - dense_1_acc_13: 0.2833 - dense_1_acc_14: 0.2333 - dense_1_acc_15: 0.1500 - dense_1_acc_16: 0.2000 - dense_1_acc_17: 0.2167 - dense_1_acc_18: 0.1333 - dense_1_acc_19: 0.1667 - dense_1_acc_20: 0.2833 - dense_1_acc_21: 0.1667 - dense_1_acc_22: 0.1500 - dense_1_acc_23: 0.1500 - dense_1_acc_24: 0.1333 - dense_1_acc_25: 0.1167 - dense_1_acc_26: 0.2500 - dense_1_acc_27: 0.1000 - dense_1_acc_28: 0.1500 - dense_1_acc_29: 0.1667 - dense_1_acc_30: 0.0000e+00         
Epoch 9/100
60/60 [==============================] - 0s - loss: 93.9753 - dense_1_loss_1: 4.2159 - dense_1_loss_2: 4.0105 - dense_1_loss_3: 3.7472 - dense_1_loss_4: 3.6921 - dense_1_loss_5: 3.4942 - dense_1_loss_6: 3.4897 - dense_1_loss_7: 3.4181 - dense_1_loss_8: 3.1503 - dense_1_loss_9: 3.1051 - dense_1_loss_10: 2.9563 - dense_1_loss_11: 3.1541 - dense_1_loss_12: 3.2926 - dense_1_loss_13: 2.9499 - dense_1_loss_14: 2.9662 - dense_1_loss_15: 3.0675 - dense_1_loss_16: 3.1146 - dense_1_loss_17: 2.9696 - dense_1_loss_18: 3.1479 - dense_1_loss_19: 3.0151 - dense_1_loss_20: 3.0469 - dense_1_loss_21: 3.0955 - dense_1_loss_22: 3.0573 - dense_1_loss_23: 3.1520 - dense_1_loss_24: 3.0335 - dense_1_loss_25: 3.3512 - dense_1_loss_26: 2.8350 - dense_1_loss_27: 3.1168 - dense_1_loss_28: 3.1114 - dense_1_loss_29: 3.2186 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1167 - dense_1_acc_3: 0.2000 - dense_1_acc_4: 0.1500 - dense_1_acc_5: 0.2500 - dense_1_acc_6: 0.1833 - dense_1_acc_7: 0.1500 - dense_1_acc_8: 0.1833 - dense_1_acc_9: 0.2667 - dense_1_acc_10: 0.2500 - dense_1_acc_11: 0.1667 - dense_1_acc_12: 0.1500 - dense_1_acc_13: 0.3333 - dense_1_acc_14: 0.2333 - dense_1_acc_15: 0.2000 - dense_1_acc_16: 0.2333 - dense_1_acc_17: 0.3000 - dense_1_acc_18: 0.1333 - dense_1_acc_19: 0.2000 - dense_1_acc_20: 0.3333 - dense_1_acc_21: 0.1833 - dense_1_acc_22: 0.1500 - dense_1_acc_23: 0.2000 - dense_1_acc_24: 0.2000 - dense_1_acc_25: 0.1167 - dense_1_acc_26: 0.2667 - dense_1_acc_27: 0.1667 - dense_1_acc_28: 0.1500 - dense_1_acc_29: 0.2000 - dense_1_acc_30: 0.0000e+00     
Epoch 10/100
60/60 [==============================] - 0s - loss: 89.7720 - dense_1_loss_1: 4.2048 - dense_1_loss_2: 3.9711 - dense_1_loss_3: 3.6677 - dense_1_loss_4: 3.6035 - dense_1_loss_5: 3.3800 - dense_1_loss_6: 3.3506 - dense_1_loss_7: 3.2899 - dense_1_loss_8: 3.0100 - dense_1_loss_9: 2.9501 - dense_1_loss_10: 2.7743 - dense_1_loss_11: 3.0100 - dense_1_loss_12: 3.0628 - dense_1_loss_13: 2.8252 - dense_1_loss_14: 2.8456 - dense_1_loss_15: 2.9193 - dense_1_loss_16: 2.9354 - dense_1_loss_17: 2.7749 - dense_1_loss_18: 3.0148 - dense_1_loss_19: 2.8805 - dense_1_loss_20: 2.8963 - dense_1_loss_21: 2.9775 - dense_1_loss_22: 2.8919 - dense_1_loss_23: 2.9468 - dense_1_loss_24: 2.8604 - dense_1_loss_25: 3.1973 - dense_1_loss_26: 2.6616 - dense_1_loss_27: 2.9519 - dense_1_loss_28: 2.9121 - dense_1_loss_29: 3.0059 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.0667 - dense_1_acc_3: 0.2000 - dense_1_acc_4: 0.1667 - dense_1_acc_5: 0.2000 - dense_1_acc_6: 0.2000 - dense_1_acc_7: 0.2167 - dense_1_acc_8: 0.1833 - dense_1_acc_9: 0.3000 - dense_1_acc_10: 0.2500 - dense_1_acc_11: 0.1667 - dense_1_acc_12: 0.1500 - dense_1_acc_13: 0.2833 - dense_1_acc_14: 0.2500 - dense_1_acc_15: 0.2500 - dense_1_acc_16: 0.2667 - dense_1_acc_17: 0.3000 - dense_1_acc_18: 0.1000 - dense_1_acc_19: 0.2167 - dense_1_acc_20: 0.2833 - dense_1_acc_21: 0.2167 - dense_1_acc_22: 0.1500 - dense_1_acc_23: 0.2333 - dense_1_acc_24: 0.1667 - dense_1_acc_25: 0.1167 - dense_1_acc_26: 0.2833 - dense_1_acc_27: 0.1667 - dense_1_acc_28: 0.2167 - dense_1_acc_29: 0.2167 - dense_1_acc_30: 0.0000e+00     
Epoch 11/100
60/60 [==============================] - 0s - loss: 85.6615 - dense_1_loss_1: 4.1942 - dense_1_loss_2: 3.9323 - dense_1_loss_3: 3.5914 - dense_1_loss_4: 3.5058 - dense_1_loss_5: 3.2599 - dense_1_loss_6: 3.2069 - dense_1_loss_7: 3.1536 - dense_1_loss_8: 2.8428 - dense_1_loss_9: 2.8446 - dense_1_loss_10: 2.6600 - dense_1_loss_11: 2.8793 - dense_1_loss_12: 2.8746 - dense_1_loss_13: 2.6513 - dense_1_loss_14: 2.6880 - dense_1_loss_15: 2.7775 - dense_1_loss_16: 2.8001 - dense_1_loss_17: 2.6575 - dense_1_loss_18: 2.8262 - dense_1_loss_19: 2.6729 - dense_1_loss_20: 2.7437 - dense_1_loss_21: 2.7738 - dense_1_loss_22: 2.7370 - dense_1_loss_23: 2.8320 - dense_1_loss_24: 2.6954 - dense_1_loss_25: 2.9728 - dense_1_loss_26: 2.5801 - dense_1_loss_27: 2.7190 - dense_1_loss_28: 2.7862 - dense_1_loss_29: 2.8024 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.0667 - dense_1_acc_3: 0.2000 - dense_1_acc_4: 0.2167 - dense_1_acc_5: 0.2833 - dense_1_acc_6: 0.2167 - dense_1_acc_7: 0.2333 - dense_1_acc_8: 0.2833 - dense_1_acc_9: 0.2667 - dense_1_acc_10: 0.3167 - dense_1_acc_11: 0.1333 - dense_1_acc_12: 0.2000 - dense_1_acc_13: 0.3333 - dense_1_acc_14: 0.2333 - dense_1_acc_15: 0.2333 - dense_1_acc_16: 0.2500 - dense_1_acc_17: 0.2333 - dense_1_acc_18: 0.1500 - dense_1_acc_19: 0.2500 - dense_1_acc_20: 0.2833 - dense_1_acc_21: 0.2333 - dense_1_acc_22: 0.2000 - dense_1_acc_23: 0.2333 - dense_1_acc_24: 0.2000 - dense_1_acc_25: 0.1667 - dense_1_acc_26: 0.3333 - dense_1_acc_27: 0.2000 - dense_1_acc_28: 0.2167 - dense_1_acc_29: 0.3500 - dense_1_acc_30: 0.0000e+00     
Epoch 12/100
60/60 [==============================] - 0s - loss: 81.9096 - dense_1_loss_1: 4.1837 - dense_1_loss_2: 3.8924 - dense_1_loss_3: 3.5047 - dense_1_loss_4: 3.4058 - dense_1_loss_5: 3.1285 - dense_1_loss_6: 3.0528 - dense_1_loss_7: 3.0213 - dense_1_loss_8: 2.6764 - dense_1_loss_9: 2.6832 - dense_1_loss_10: 2.5371 - dense_1_loss_11: 2.7424 - dense_1_loss_12: 2.7007 - dense_1_loss_13: 2.5169 - dense_1_loss_14: 2.5984 - dense_1_loss_15: 2.5748 - dense_1_loss_16: 2.6452 - dense_1_loss_17: 2.5546 - dense_1_loss_18: 2.6831 - dense_1_loss_19: 2.6039 - dense_1_loss_20: 2.6078 - dense_1_loss_21: 2.6546 - dense_1_loss_22: 2.5963 - dense_1_loss_23: 2.6691 - dense_1_loss_24: 2.6460 - dense_1_loss_25: 2.8278 - dense_1_loss_26: 2.3809 - dense_1_loss_27: 2.6169 - dense_1_loss_28: 2.5561 - dense_1_loss_29: 2.6480 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1167 - dense_1_acc_3: 0.2167 - dense_1_acc_4: 0.2167 - dense_1_acc_5: 0.3000 - dense_1_acc_6: 0.2333 - dense_1_acc_7: 0.2833 - dense_1_acc_8: 0.3167 - dense_1_acc_9: 0.3167 - dense_1_acc_10: 0.3000 - dense_1_acc_11: 0.1667 - dense_1_acc_12: 0.2500 - dense_1_acc_13: 0.4000 - dense_1_acc_14: 0.2833 - dense_1_acc_15: 0.2500 - dense_1_acc_16: 0.2500 - dense_1_acc_17: 0.2500 - dense_1_acc_18: 0.1500 - dense_1_acc_19: 0.2833 - dense_1_acc_20: 0.3167 - dense_1_acc_21: 0.2667 - dense_1_acc_22: 0.2333 - dense_1_acc_23: 0.2333 - dense_1_acc_24: 0.2500 - dense_1_acc_25: 0.1333 - dense_1_acc_26: 0.3833 - dense_1_acc_27: 0.3000 - dense_1_acc_28: 0.3167 - dense_1_acc_29: 0.3167 - dense_1_acc_30: 0.0000e+00     
Epoch 13/100
60/60 [==============================] - 0s - loss: 77.9424 - dense_1_loss_1: 4.1726 - dense_1_loss_2: 3.8520 - dense_1_loss_3: 3.4239 - dense_1_loss_4: 3.3049 - dense_1_loss_5: 3.0094 - dense_1_loss_6: 2.9040 - dense_1_loss_7: 2.8879 - dense_1_loss_8: 2.5388 - dense_1_loss_9: 2.5642 - dense_1_loss_10: 2.3932 - dense_1_loss_11: 2.5736 - dense_1_loss_12: 2.5587 - dense_1_loss_13: 2.3320 - dense_1_loss_14: 2.4560 - dense_1_loss_15: 2.4168 - dense_1_loss_16: 2.5107 - dense_1_loss_17: 2.3550 - dense_1_loss_18: 2.4863 - dense_1_loss_19: 2.4692 - dense_1_loss_20: 2.4468 - dense_1_loss_21: 2.5056 - dense_1_loss_22: 2.4056 - dense_1_loss_23: 2.4519 - dense_1_loss_24: 2.6144 - dense_1_loss_25: 2.6999 - dense_1_loss_26: 2.1690 - dense_1_loss_27: 2.4230 - dense_1_loss_28: 2.4840 - dense_1_loss_29: 2.5331 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1167 - dense_1_acc_3: 0.2500 - dense_1_acc_4: 0.2000 - dense_1_acc_5: 0.3167 - dense_1_acc_6: 0.2167 - dense_1_acc_7: 0.3000 - dense_1_acc_8: 0.3500 - dense_1_acc_9: 0.3333 - dense_1_acc_10: 0.3333 - dense_1_acc_11: 0.2833 - dense_1_acc_12: 0.2500 - dense_1_acc_13: 0.3667 - dense_1_acc_14: 0.2500 - dense_1_acc_15: 0.2667 - dense_1_acc_16: 0.2333 - dense_1_acc_17: 0.3167 - dense_1_acc_18: 0.1833 - dense_1_acc_19: 0.2667 - dense_1_acc_20: 0.3333 - dense_1_acc_21: 0.3000 - dense_1_acc_22: 0.3000 - dense_1_acc_23: 0.2833 - dense_1_acc_24: 0.2500 - dense_1_acc_25: 0.1833 - dense_1_acc_26: 0.4167 - dense_1_acc_27: 0.2500 - dense_1_acc_28: 0.2167 - dense_1_acc_29: 0.3167 - dense_1_acc_30: 0.0000e+00     
Epoch 14/100
60/60 [==============================] - 0s - loss: 74.5680 - dense_1_loss_1: 4.1639 - dense_1_loss_2: 3.8115 - dense_1_loss_3: 3.3439 - dense_1_loss_4: 3.1987 - dense_1_loss_5: 2.8879 - dense_1_loss_6: 2.7570 - dense_1_loss_7: 2.7657 - dense_1_loss_8: 2.4219 - dense_1_loss_9: 2.4471 - dense_1_loss_10: 2.2721 - dense_1_loss_11: 2.4152 - dense_1_loss_12: 2.4041 - dense_1_loss_13: 2.1848 - dense_1_loss_14: 2.3034 - dense_1_loss_15: 2.2661 - dense_1_loss_16: 2.3730 - dense_1_loss_17: 2.2420 - dense_1_loss_18: 2.3084 - dense_1_loss_19: 2.3039 - dense_1_loss_20: 2.3927 - dense_1_loss_21: 2.3191 - dense_1_loss_22: 2.2784 - dense_1_loss_23: 2.3497 - dense_1_loss_24: 2.4033 - dense_1_loss_25: 2.6364 - dense_1_loss_26: 2.1220 - dense_1_loss_27: 2.3866 - dense_1_loss_28: 2.4073 - dense_1_loss_29: 2.4020 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1500 - dense_1_acc_3: 0.2500 - dense_1_acc_4: 0.2500 - dense_1_acc_5: 0.3333 - dense_1_acc_6: 0.2833 - dense_1_acc_7: 0.2667 - dense_1_acc_8: 0.3167 - dense_1_acc_9: 0.3333 - dense_1_acc_10: 0.3667 - dense_1_acc_11: 0.3167 - dense_1_acc_12: 0.2333 - dense_1_acc_13: 0.4000 - dense_1_acc_14: 0.3500 - dense_1_acc_15: 0.3500 - dense_1_acc_16: 0.2833 - dense_1_acc_17: 0.3667 - dense_1_acc_18: 0.2000 - dense_1_acc_19: 0.2833 - dense_1_acc_20: 0.3000 - dense_1_acc_21: 0.2833 - dense_1_acc_22: 0.2833 - dense_1_acc_23: 0.3333 - dense_1_acc_24: 0.2333 - dense_1_acc_25: 0.1667 - dense_1_acc_26: 0.3833 - dense_1_acc_27: 0.2667 - dense_1_acc_28: 0.2333 - dense_1_acc_29: 0.2833 - dense_1_acc_30: 0.0000e+00     
Epoch 15/100
60/60 [==============================] - 0s - loss: 70.7818 - dense_1_loss_1: 4.1566 - dense_1_loss_2: 3.7716 - dense_1_loss_3: 3.2704 - dense_1_loss_4: 3.1003 - dense_1_loss_5: 2.7766 - dense_1_loss_6: 2.6157 - dense_1_loss_7: 2.6423 - dense_1_loss_8: 2.3160 - dense_1_loss_9: 2.3343 - dense_1_loss_10: 2.1863 - dense_1_loss_11: 2.3080 - dense_1_loss_12: 2.2695 - dense_1_loss_13: 2.0643 - dense_1_loss_14: 2.1616 - dense_1_loss_15: 2.2092 - dense_1_loss_16: 2.2644 - dense_1_loss_17: 2.1717 - dense_1_loss_18: 2.1806 - dense_1_loss_19: 2.1495 - dense_1_loss_20: 2.2528 - dense_1_loss_21: 2.0959 - dense_1_loss_22: 2.1184 - dense_1_loss_23: 2.2349 - dense_1_loss_24: 2.2799 - dense_1_loss_25: 2.4104 - dense_1_loss_26: 1.9154 - dense_1_loss_27: 2.1144 - dense_1_loss_28: 2.2116 - dense_1_loss_29: 2.1992 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1500 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2667 - dense_1_acc_5: 0.3167 - dense_1_acc_6: 0.3167 - dense_1_acc_7: 0.3167 - dense_1_acc_8: 0.3500 - dense_1_acc_9: 0.4000 - dense_1_acc_10: 0.4000 - dense_1_acc_11: 0.2333 - dense_1_acc_12: 0.2333 - dense_1_acc_13: 0.4667 - dense_1_acc_14: 0.4167 - dense_1_acc_15: 0.2833 - dense_1_acc_16: 0.3167 - dense_1_acc_17: 0.3667 - dense_1_acc_18: 0.3167 - dense_1_acc_19: 0.3500 - dense_1_acc_20: 0.2833 - dense_1_acc_21: 0.3500 - dense_1_acc_22: 0.3667 - dense_1_acc_23: 0.4000 - dense_1_acc_24: 0.3000 - dense_1_acc_25: 0.2000 - dense_1_acc_26: 0.5167 - dense_1_acc_27: 0.3833 - dense_1_acc_28: 0.4167 - dense_1_acc_29: 0.4333 - dense_1_acc_30: 0.0000e+00     
Epoch 16/100
60/60 [==============================] - 0s - loss: 67.6264 - dense_1_loss_1: 4.1490 - dense_1_loss_2: 3.7330 - dense_1_loss_3: 3.1997 - dense_1_loss_4: 2.9972 - dense_1_loss_5: 2.6689 - dense_1_loss_6: 2.4691 - dense_1_loss_7: 2.4959 - dense_1_loss_8: 2.2321 - dense_1_loss_9: 2.2149 - dense_1_loss_10: 2.0676 - dense_1_loss_11: 2.1944 - dense_1_loss_12: 2.0894 - dense_1_loss_13: 1.9174 - dense_1_loss_14: 2.0482 - dense_1_loss_15: 2.0521 - dense_1_loss_16: 2.1589 - dense_1_loss_17: 2.0443 - dense_1_loss_18: 2.0343 - dense_1_loss_19: 2.0277 - dense_1_loss_20: 2.0924 - dense_1_loss_21: 2.0356 - dense_1_loss_22: 2.0433 - dense_1_loss_23: 2.1854 - dense_1_loss_24: 2.1334 - dense_1_loss_25: 2.2683 - dense_1_loss_26: 1.8710 - dense_1_loss_27: 2.0543 - dense_1_loss_28: 2.0875 - dense_1_loss_29: 2.0611 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1833 - dense_1_acc_3: 0.3000 - dense_1_acc_4: 0.2667 - dense_1_acc_5: 0.3333 - dense_1_acc_6: 0.3167 - dense_1_acc_7: 0.3500 - dense_1_acc_8: 0.3833 - dense_1_acc_9: 0.3833 - dense_1_acc_10: 0.3500 - dense_1_acc_11: 0.2500 - dense_1_acc_12: 0.3667 - dense_1_acc_13: 0.4167 - dense_1_acc_14: 0.4167 - dense_1_acc_15: 0.3333 - dense_1_acc_16: 0.3833 - dense_1_acc_17: 0.4000 - dense_1_acc_18: 0.4000 - dense_1_acc_19: 0.3833 - dense_1_acc_20: 0.4167 - dense_1_acc_21: 0.3833 - dense_1_acc_22: 0.3333 - dense_1_acc_23: 0.3000 - dense_1_acc_24: 0.3333 - dense_1_acc_25: 0.2167 - dense_1_acc_26: 0.4667 - dense_1_acc_27: 0.3500 - dense_1_acc_28: 0.4333 - dense_1_acc_29: 0.4333 - dense_1_acc_30: 0.0000e+00     
Epoch 17/100
60/60 [==============================] - 0s - loss: 64.3102 - dense_1_loss_1: 4.1432 - dense_1_loss_2: 3.6922 - dense_1_loss_3: 3.1260 - dense_1_loss_4: 2.9039 - dense_1_loss_5: 2.5473 - dense_1_loss_6: 2.3139 - dense_1_loss_7: 2.3524 - dense_1_loss_8: 2.1075 - dense_1_loss_9: 2.1829 - dense_1_loss_10: 1.9446 - dense_1_loss_11: 2.1464 - dense_1_loss_12: 2.0344 - dense_1_loss_13: 1.8492 - dense_1_loss_14: 1.8603 - dense_1_loss_15: 1.9291 - dense_1_loss_16: 2.0644 - dense_1_loss_17: 1.9326 - dense_1_loss_18: 1.8428 - dense_1_loss_19: 1.9004 - dense_1_loss_20: 1.9474 - dense_1_loss_21: 1.9269 - dense_1_loss_22: 1.9244 - dense_1_loss_23: 1.9607 - dense_1_loss_24: 2.0257 - dense_1_loss_25: 2.1022 - dense_1_loss_26: 1.7460 - dense_1_loss_27: 1.8937 - dense_1_loss_28: 1.9563 - dense_1_loss_29: 1.9534 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2000 - dense_1_acc_3: 0.3500 - dense_1_acc_4: 0.2667 - dense_1_acc_5: 0.3333 - dense_1_acc_6: 0.3500 - dense_1_acc_7: 0.3500 - dense_1_acc_8: 0.3667 - dense_1_acc_9: 0.3667 - dense_1_acc_10: 0.4333 - dense_1_acc_11: 0.3000 - dense_1_acc_12: 0.3500 - dense_1_acc_13: 0.4333 - dense_1_acc_14: 0.4167 - dense_1_acc_15: 0.3667 - dense_1_acc_16: 0.3333 - dense_1_acc_17: 0.3833 - dense_1_acc_18: 0.4833 - dense_1_acc_19: 0.4500 - dense_1_acc_20: 0.4667 - dense_1_acc_21: 0.4167 - dense_1_acc_22: 0.3667 - dense_1_acc_23: 0.4167 - dense_1_acc_24: 0.3667 - dense_1_acc_25: 0.2833 - dense_1_acc_26: 0.5667 - dense_1_acc_27: 0.4500 - dense_1_acc_28: 0.4167 - dense_1_acc_29: 0.4667 - dense_1_acc_30: 0.0000e+00     
Epoch 18/100
60/60 [==============================] - 0s - loss: 60.9770 - dense_1_loss_1: 4.1352 - dense_1_loss_2: 3.6501 - dense_1_loss_3: 3.0557 - dense_1_loss_4: 2.8116 - dense_1_loss_5: 2.4615 - dense_1_loss_6: 2.2039 - dense_1_loss_7: 2.2144 - dense_1_loss_8: 1.9720 - dense_1_loss_9: 2.0354 - dense_1_loss_10: 1.8256 - dense_1_loss_11: 1.9682 - dense_1_loss_12: 1.8455 - dense_1_loss_13: 1.7386 - dense_1_loss_14: 1.7591 - dense_1_loss_15: 1.7897 - dense_1_loss_16: 1.9169 - dense_1_loss_17: 1.8054 - dense_1_loss_18: 1.8099 - dense_1_loss_19: 1.7484 - dense_1_loss_20: 1.7715 - dense_1_loss_21: 1.7874 - dense_1_loss_22: 1.8334 - dense_1_loss_23: 1.7951 - dense_1_loss_24: 1.9296 - dense_1_loss_25: 1.9762 - dense_1_loss_26: 1.6691 - dense_1_loss_27: 1.8107 - dense_1_loss_28: 1.8523 - dense_1_loss_29: 1.8047 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2167 - dense_1_acc_3: 0.3500 - dense_1_acc_4: 0.2500 - dense_1_acc_5: 0.3667 - dense_1_acc_6: 0.3333 - dense_1_acc_7: 0.4000 - dense_1_acc_8: 0.4333 - dense_1_acc_9: 0.3667 - dense_1_acc_10: 0.4500 - dense_1_acc_11: 0.3833 - dense_1_acc_12: 0.4167 - dense_1_acc_13: 0.5333 - dense_1_acc_14: 0.4667 - dense_1_acc_15: 0.4667 - dense_1_acc_16: 0.3333 - dense_1_acc_17: 0.4000 - dense_1_acc_18: 0.3667 - dense_1_acc_19: 0.4000 - dense_1_acc_20: 0.4833 - dense_1_acc_21: 0.3833 - dense_1_acc_22: 0.4500 - dense_1_acc_23: 0.4833 - dense_1_acc_24: 0.3500 - dense_1_acc_25: 0.3500 - dense_1_acc_26: 0.5333 - dense_1_acc_27: 0.4333 - dense_1_acc_28: 0.4333 - dense_1_acc_29: 0.5500 - dense_1_acc_30: 0.0000e+00     
Epoch 19/100
60/60 [==============================] - 0s - loss: 58.1739 - dense_1_loss_1: 4.1267 - dense_1_loss_2: 3.6067 - dense_1_loss_3: 2.9783 - dense_1_loss_4: 2.7143 - dense_1_loss_5: 2.3603 - dense_1_loss_6: 2.1084 - dense_1_loss_7: 2.1157 - dense_1_loss_8: 1.8884 - dense_1_loss_9: 1.9336 - dense_1_loss_10: 1.7485 - dense_1_loss_11: 1.9035 - dense_1_loss_12: 1.7516 - dense_1_loss_13: 1.5965 - dense_1_loss_14: 1.6437 - dense_1_loss_15: 1.6844 - dense_1_loss_16: 1.8346 - dense_1_loss_17: 1.7095 - dense_1_loss_18: 1.7362 - dense_1_loss_19: 1.6973 - dense_1_loss_20: 1.6533 - dense_1_loss_21: 1.6370 - dense_1_loss_22: 1.7230 - dense_1_loss_23: 1.7123 - dense_1_loss_24: 1.7885 - dense_1_loss_25: 1.8111 - dense_1_loss_26: 1.6029 - dense_1_loss_27: 1.7325 - dense_1_loss_28: 1.7083 - dense_1_loss_29: 1.6667 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2167 - dense_1_acc_3: 0.3667 - dense_1_acc_4: 0.2500 - dense_1_acc_5: 0.3500 - dense_1_acc_6: 0.3833 - dense_1_acc_7: 0.4000 - dense_1_acc_8: 0.4167 - dense_1_acc_9: 0.4333 - dense_1_acc_10: 0.4500 - dense_1_acc_11: 0.3167 - dense_1_acc_12: 0.5000 - dense_1_acc_13: 0.6000 - dense_1_acc_14: 0.5000 - dense_1_acc_15: 0.5333 - dense_1_acc_16: 0.3833 - dense_1_acc_17: 0.4833 - dense_1_acc_18: 0.3833 - dense_1_acc_19: 0.4333 - dense_1_acc_20: 0.5000 - dense_1_acc_21: 0.5000 - dense_1_acc_22: 0.4833 - dense_1_acc_23: 0.4833 - dense_1_acc_24: 0.4167 - dense_1_acc_25: 0.4333 - dense_1_acc_26: 0.6333 - dense_1_acc_27: 0.5000 - dense_1_acc_28: 0.5167 - dense_1_acc_29: 0.5667 - dense_1_acc_30: 0.0000e+00     
Epoch 20/100
60/60 [==============================] - 0s - loss: 55.4761 - dense_1_loss_1: 4.1189 - dense_1_loss_2: 3.5637 - dense_1_loss_3: 2.9002 - dense_1_loss_4: 2.6177 - dense_1_loss_5: 2.2596 - dense_1_loss_6: 1.9893 - dense_1_loss_7: 2.0135 - dense_1_loss_8: 1.7673 - dense_1_loss_9: 1.8833 - dense_1_loss_10: 1.6986 - dense_1_loss_11: 1.7912 - dense_1_loss_12: 1.6945 - dense_1_loss_13: 1.5218 - dense_1_loss_14: 1.5522 - dense_1_loss_15: 1.6312 - dense_1_loss_16: 1.7254 - dense_1_loss_17: 1.6705 - dense_1_loss_18: 1.5892 - dense_1_loss_19: 1.6236 - dense_1_loss_20: 1.5906 - dense_1_loss_21: 1.5903 - dense_1_loss_22: 1.6394 - dense_1_loss_23: 1.5444 - dense_1_loss_24: 1.6563 - dense_1_loss_25: 1.7084 - dense_1_loss_26: 1.4733 - dense_1_loss_27: 1.5569 - dense_1_loss_28: 1.5812 - dense_1_loss_29: 1.5237 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2667 - dense_1_acc_3: 0.3833 - dense_1_acc_4: 0.2500 - dense_1_acc_5: 0.3500 - dense_1_acc_6: 0.4000 - dense_1_acc_7: 0.4000 - dense_1_acc_8: 0.4167 - dense_1_acc_9: 0.4167 - dense_1_acc_10: 0.4667 - dense_1_acc_11: 0.4167 - dense_1_acc_12: 0.4000 - dense_1_acc_13: 0.6000 - dense_1_acc_14: 0.5500 - dense_1_acc_15: 0.4833 - dense_1_acc_16: 0.4667 - dense_1_acc_17: 0.4667 - dense_1_acc_18: 0.4833 - dense_1_acc_19: 0.5000 - dense_1_acc_20: 0.5833 - dense_1_acc_21: 0.5833 - dense_1_acc_22: 0.5167 - dense_1_acc_23: 0.6167 - dense_1_acc_24: 0.5333 - dense_1_acc_25: 0.4167 - dense_1_acc_26: 0.6500 - dense_1_acc_27: 0.6000 - dense_1_acc_28: 0.5167 - dense_1_acc_29: 0.6167 - dense_1_acc_30: 0.0000e+00     
Epoch 21/100
60/60 [==============================] - 0s - loss: 52.5952 - dense_1_loss_1: 4.1115 - dense_1_loss_2: 3.5210 - dense_1_loss_3: 2.8198 - dense_1_loss_4: 2.5191 - dense_1_loss_5: 2.1622 - dense_1_loss_6: 1.8718 - dense_1_loss_7: 1.8840 - dense_1_loss_8: 1.6437 - dense_1_loss_9: 1.7017 - dense_1_loss_10: 1.5723 - dense_1_loss_11: 1.6463 - dense_1_loss_12: 1.5608 - dense_1_loss_13: 1.3714 - dense_1_loss_14: 1.4084 - dense_1_loss_15: 1.4898 - dense_1_loss_16: 1.5919 - dense_1_loss_17: 1.5521 - dense_1_loss_18: 1.4812 - dense_1_loss_19: 1.4532 - dense_1_loss_20: 1.5159 - dense_1_loss_21: 1.4975 - dense_1_loss_22: 1.5416 - dense_1_loss_23: 1.4791 - dense_1_loss_24: 1.5756 - dense_1_loss_25: 1.6586 - dense_1_loss_26: 1.4051 - dense_1_loss_27: 1.5384 - dense_1_loss_28: 1.5311 - dense_1_loss_29: 1.4903 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2667 - dense_1_acc_3: 0.4000 - dense_1_acc_4: 0.2667 - dense_1_acc_5: 0.3500 - dense_1_acc_6: 0.4500 - dense_1_acc_7: 0.4500 - dense_1_acc_8: 0.5000 - dense_1_acc_9: 0.5167 - dense_1_acc_10: 0.5000 - dense_1_acc_11: 0.4333 - dense_1_acc_12: 0.4667 - dense_1_acc_13: 0.7167 - dense_1_acc_14: 0.6833 - dense_1_acc_15: 0.5167 - dense_1_acc_16: 0.5500 - dense_1_acc_17: 0.4833 - dense_1_acc_18: 0.5333 - dense_1_acc_19: 0.5333 - dense_1_acc_20: 0.4833 - dense_1_acc_21: 0.6167 - dense_1_acc_22: 0.5667 - dense_1_acc_23: 0.5667 - dense_1_acc_24: 0.4833 - dense_1_acc_25: 0.4500 - dense_1_acc_26: 0.6333 - dense_1_acc_27: 0.5500 - dense_1_acc_28: 0.5833 - dense_1_acc_29: 0.6500 - dense_1_acc_30: 0.0000e+00     
Epoch 22/100
60/60 [==============================] - 0s - loss: 50.2160 - dense_1_loss_1: 4.1047 - dense_1_loss_2: 3.4770 - dense_1_loss_3: 2.7407 - dense_1_loss_4: 2.4178 - dense_1_loss_5: 2.0648 - dense_1_loss_6: 1.7635 - dense_1_loss_7: 1.7659 - dense_1_loss_8: 1.5881 - dense_1_loss_9: 1.5796 - dense_1_loss_10: 1.4720 - dense_1_loss_11: 1.5638 - dense_1_loss_12: 1.4441 - dense_1_loss_13: 1.3000 - dense_1_loss_14: 1.3932 - dense_1_loss_15: 1.3870 - dense_1_loss_16: 1.5121 - dense_1_loss_17: 1.4827 - dense_1_loss_18: 1.3958 - dense_1_loss_19: 1.4016 - dense_1_loss_20: 1.4361 - dense_1_loss_21: 1.4005 - dense_1_loss_22: 1.5129 - dense_1_loss_23: 1.3737 - dense_1_loss_24: 1.4531 - dense_1_loss_25: 1.5305 - dense_1_loss_26: 1.3757 - dense_1_loss_27: 1.4563 - dense_1_loss_28: 1.4114 - dense_1_loss_29: 1.4113 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2667 - dense_1_acc_3: 0.4167 - dense_1_acc_4: 0.3000 - dense_1_acc_5: 0.3667 - dense_1_acc_6: 0.5000 - dense_1_acc_7: 0.4667 - dense_1_acc_8: 0.5167 - dense_1_acc_9: 0.6000 - dense_1_acc_10: 0.5500 - dense_1_acc_11: 0.4333 - dense_1_acc_12: 0.5167 - dense_1_acc_13: 0.7000 - dense_1_acc_14: 0.6333 - dense_1_acc_15: 0.6000 - dense_1_acc_16: 0.5333 - dense_1_acc_17: 0.5500 - dense_1_acc_18: 0.5833 - dense_1_acc_19: 0.6333 - dense_1_acc_20: 0.6833 - dense_1_acc_21: 0.6500 - dense_1_acc_22: 0.6167 - dense_1_acc_23: 0.6833 - dense_1_acc_24: 0.5667 - dense_1_acc_25: 0.5333 - dense_1_acc_26: 0.6500 - dense_1_acc_27: 0.5167 - dense_1_acc_28: 0.7000 - dense_1_acc_29: 0.6667 - dense_1_acc_30: 0.0000e+00     
Epoch 23/100
60/60 [==============================] - 0s - loss: 47.6829 - dense_1_loss_1: 4.0972 - dense_1_loss_2: 3.4353 - dense_1_loss_3: 2.6637 - dense_1_loss_4: 2.3184 - dense_1_loss_5: 1.9563 - dense_1_loss_6: 1.6456 - dense_1_loss_7: 1.6569 - dense_1_loss_8: 1.4727 - dense_1_loss_9: 1.5131 - dense_1_loss_10: 1.3883 - dense_1_loss_11: 1.4958 - dense_1_loss_12: 1.3610 - dense_1_loss_13: 1.2473 - dense_1_loss_14: 1.3105 - dense_1_loss_15: 1.3116 - dense_1_loss_16: 1.3763 - dense_1_loss_17: 1.3985 - dense_1_loss_18: 1.3418 - dense_1_loss_19: 1.3085 - dense_1_loss_20: 1.3157 - dense_1_loss_21: 1.3183 - dense_1_loss_22: 1.4045 - dense_1_loss_23: 1.3021 - dense_1_loss_24: 1.3491 - dense_1_loss_25: 1.4308 - dense_1_loss_26: 1.2834 - dense_1_loss_27: 1.3413 - dense_1_loss_28: 1.3298 - dense_1_loss_29: 1.3091 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2667 - dense_1_acc_3: 0.4333 - dense_1_acc_4: 0.3333 - dense_1_acc_5: 0.4000 - dense_1_acc_6: 0.5000 - dense_1_acc_7: 0.5000 - dense_1_acc_8: 0.5667 - dense_1_acc_9: 0.6167 - dense_1_acc_10: 0.6167 - dense_1_acc_11: 0.5667 - dense_1_acc_12: 0.6167 - dense_1_acc_13: 0.7333 - dense_1_acc_14: 0.6167 - dense_1_acc_15: 0.5833 - dense_1_acc_16: 0.6000 - dense_1_acc_17: 0.6333 - dense_1_acc_18: 0.6500 - dense_1_acc_19: 0.6833 - dense_1_acc_20: 0.7667 - dense_1_acc_21: 0.6500 - dense_1_acc_22: 0.6500 - dense_1_acc_23: 0.7333 - dense_1_acc_24: 0.6667 - dense_1_acc_25: 0.5333 - dense_1_acc_26: 0.7333 - dense_1_acc_27: 0.6667 - dense_1_acc_28: 0.7167 - dense_1_acc_29: 0.7000 - dense_1_acc_30: 0.0000e+00     
Epoch 24/100
60/60 [==============================] - 0s - loss: 45.3187 - dense_1_loss_1: 4.0901 - dense_1_loss_2: 3.3922 - dense_1_loss_3: 2.5825 - dense_1_loss_4: 2.2373 - dense_1_loss_5: 1.8703 - dense_1_loss_6: 1.5549 - dense_1_loss_7: 1.5355 - dense_1_loss_8: 1.4056 - dense_1_loss_9: 1.3904 - dense_1_loss_10: 1.3019 - dense_1_loss_11: 1.3719 - dense_1_loss_12: 1.2736 - dense_1_loss_13: 1.1612 - dense_1_loss_14: 1.2376 - dense_1_loss_15: 1.2141 - dense_1_loss_16: 1.2789 - dense_1_loss_17: 1.3098 - dense_1_loss_18: 1.2803 - dense_1_loss_19: 1.2623 - dense_1_loss_20: 1.2124 - dense_1_loss_21: 1.2315 - dense_1_loss_22: 1.3501 - dense_1_loss_23: 1.2118 - dense_1_loss_24: 1.2614 - dense_1_loss_25: 1.3370 - dense_1_loss_26: 1.2148 - dense_1_loss_27: 1.2702 - dense_1_loss_28: 1.2633 - dense_1_loss_29: 1.2155 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2833 - dense_1_acc_3: 0.4500 - dense_1_acc_4: 0.3500 - dense_1_acc_5: 0.4000 - dense_1_acc_6: 0.6000 - dense_1_acc_7: 0.5500 - dense_1_acc_8: 0.6333 - dense_1_acc_9: 0.6333 - dense_1_acc_10: 0.6500 - dense_1_acc_11: 0.5333 - dense_1_acc_12: 0.6833 - dense_1_acc_13: 0.8000 - dense_1_acc_14: 0.6000 - dense_1_acc_15: 0.6000 - dense_1_acc_16: 0.6667 - dense_1_acc_17: 0.6833 - dense_1_acc_18: 0.7167 - dense_1_acc_19: 0.7333 - dense_1_acc_20: 0.7500 - dense_1_acc_21: 0.7500 - dense_1_acc_22: 0.7000 - dense_1_acc_23: 0.7667 - dense_1_acc_24: 0.7500 - dense_1_acc_25: 0.5000 - dense_1_acc_26: 0.7167 - dense_1_acc_27: 0.6667 - dense_1_acc_28: 0.7667 - dense_1_acc_29: 0.7667 - dense_1_acc_30: 0.0000e+00     
Epoch 25/100
60/60 [==============================] - 0s - loss: 43.0943 - dense_1_loss_1: 4.0826 - dense_1_loss_2: 3.3473 - dense_1_loss_3: 2.5037 - dense_1_loss_4: 2.1542 - dense_1_loss_5: 1.7748 - dense_1_loss_6: 1.4676 - dense_1_loss_7: 1.4258 - dense_1_loss_8: 1.3439 - dense_1_loss_9: 1.2775 - dense_1_loss_10: 1.2072 - dense_1_loss_11: 1.2504 - dense_1_loss_12: 1.1964 - dense_1_loss_13: 1.0910 - dense_1_loss_14: 1.1317 - dense_1_loss_15: 1.1530 - dense_1_loss_16: 1.1781 - dense_1_loss_17: 1.2662 - dense_1_loss_18: 1.2050 - dense_1_loss_19: 1.1581 - dense_1_loss_20: 1.1413 - dense_1_loss_21: 1.1878 - dense_1_loss_22: 1.2693 - dense_1_loss_23: 1.1599 - dense_1_loss_24: 1.1910 - dense_1_loss_25: 1.2612 - dense_1_loss_26: 1.1298 - dense_1_loss_27: 1.2135 - dense_1_loss_28: 1.1730 - dense_1_loss_29: 1.1530 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4667 - dense_1_acc_4: 0.3500 - dense_1_acc_5: 0.4500 - dense_1_acc_6: 0.6000 - dense_1_acc_7: 0.5833 - dense_1_acc_8: 0.6500 - dense_1_acc_9: 0.6833 - dense_1_acc_10: 0.6833 - dense_1_acc_11: 0.6167 - dense_1_acc_12: 0.7167 - dense_1_acc_13: 0.8333 - dense_1_acc_14: 0.7167 - dense_1_acc_15: 0.7000 - dense_1_acc_16: 0.7167 - dense_1_acc_17: 0.6333 - dense_1_acc_18: 0.7000 - dense_1_acc_19: 0.8000 - dense_1_acc_20: 0.7833 - dense_1_acc_21: 0.7667 - dense_1_acc_22: 0.7000 - dense_1_acc_23: 0.7833 - dense_1_acc_24: 0.6833 - dense_1_acc_25: 0.5833 - dense_1_acc_26: 0.7500 - dense_1_acc_27: 0.6500 - dense_1_acc_28: 0.7833 - dense_1_acc_29: 0.7667 - dense_1_acc_30: 0.0000e+00     
Epoch 26/100
60/60 [==============================] - 0s - loss: 40.8022 - dense_1_loss_1: 4.0748 - dense_1_loss_2: 3.3002 - dense_1_loss_3: 2.4259 - dense_1_loss_4: 2.0715 - dense_1_loss_5: 1.6825 - dense_1_loss_6: 1.3776 - dense_1_loss_7: 1.3323 - dense_1_loss_8: 1.2728 - dense_1_loss_9: 1.2005 - dense_1_loss_10: 1.1305 - dense_1_loss_11: 1.1444 - dense_1_loss_12: 1.1230 - dense_1_loss_13: 1.0321 - dense_1_loss_14: 1.0275 - dense_1_loss_15: 1.0911 - dense_1_loss_16: 1.0935 - dense_1_loss_17: 1.1465 - dense_1_loss_18: 1.1157 - dense_1_loss_19: 1.0581 - dense_1_loss_20: 1.0947 - dense_1_loss_21: 1.1024 - dense_1_loss_22: 1.1725 - dense_1_loss_23: 1.0688 - dense_1_loss_24: 1.0804 - dense_1_loss_25: 1.1933 - dense_1_loss_26: 1.0530 - dense_1_loss_27: 1.1423 - dense_1_loss_28: 1.0956 - dense_1_loss_29: 1.0991 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2833 - dense_1_acc_3: 0.5000 - dense_1_acc_4: 0.3833 - dense_1_acc_5: 0.5000 - dense_1_acc_6: 0.6833 - dense_1_acc_7: 0.7000 - dense_1_acc_8: 0.6167 - dense_1_acc_9: 0.7000 - dense_1_acc_10: 0.7833 - dense_1_acc_11: 0.7167 - dense_1_acc_12: 0.8000 - dense_1_acc_13: 0.8833 - dense_1_acc_14: 0.8667 - dense_1_acc_15: 0.7833 - dense_1_acc_16: 0.8167 - dense_1_acc_17: 0.8000 - dense_1_acc_18: 0.7333 - dense_1_acc_19: 0.8333 - dense_1_acc_20: 0.8167 - dense_1_acc_21: 0.8667 - dense_1_acc_22: 0.7667 - dense_1_acc_23: 0.8500 - dense_1_acc_24: 0.8000 - dense_1_acc_25: 0.7000 - dense_1_acc_26: 0.8667 - dense_1_acc_27: 0.7667 - dense_1_acc_28: 0.8333 - dense_1_acc_29: 0.8333 - dense_1_acc_30: 0.0000e+00     
Epoch 27/100
60/60 [==============================] - 0s - loss: 38.8025 - dense_1_loss_1: 4.0663 - dense_1_loss_2: 3.2536 - dense_1_loss_3: 2.3506 - dense_1_loss_4: 1.9905 - dense_1_loss_5: 1.5981 - dense_1_loss_6: 1.3005 - dense_1_loss_7: 1.2525 - dense_1_loss_8: 1.2083 - dense_1_loss_9: 1.1123 - dense_1_loss_10: 1.0546 - dense_1_loss_11: 1.0712 - dense_1_loss_12: 1.0661 - dense_1_loss_13: 0.9613 - dense_1_loss_14: 0.9802 - dense_1_loss_15: 1.0168 - dense_1_loss_16: 1.0461 - dense_1_loss_17: 1.0501 - dense_1_loss_18: 1.0304 - dense_1_loss_19: 1.0029 - dense_1_loss_20: 1.0419 - dense_1_loss_21: 1.0439 - dense_1_loss_22: 1.0489 - dense_1_loss_23: 1.0150 - dense_1_loss_24: 0.9941 - dense_1_loss_25: 1.1266 - dense_1_loss_26: 1.0159 - dense_1_loss_27: 1.0529 - dense_1_loss_28: 1.0263 - dense_1_loss_29: 1.0247 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2833 - dense_1_acc_3: 0.5000 - dense_1_acc_4: 0.4000 - dense_1_acc_5: 0.5167 - dense_1_acc_6: 0.6667 - dense_1_acc_7: 0.7500 - dense_1_acc_8: 0.6500 - dense_1_acc_9: 0.7500 - dense_1_acc_10: 0.8333 - dense_1_acc_11: 0.7667 - dense_1_acc_12: 0.8167 - dense_1_acc_13: 0.8833 - dense_1_acc_14: 0.8667 - dense_1_acc_15: 0.8333 - dense_1_acc_16: 0.9000 - dense_1_acc_17: 0.8500 - dense_1_acc_18: 0.7500 - dense_1_acc_19: 0.8833 - dense_1_acc_20: 0.8500 - dense_1_acc_21: 0.8500 - dense_1_acc_22: 0.8833 - dense_1_acc_23: 0.8333 - dense_1_acc_24: 0.8667 - dense_1_acc_25: 0.7000 - dense_1_acc_26: 0.8500 - dense_1_acc_27: 0.8000 - dense_1_acc_28: 0.8333 - dense_1_acc_29: 0.8333 - dense_1_acc_30: 0.0000e+00     
Epoch 28/100
60/60 [==============================] - 0s - loss: 36.8764 - dense_1_loss_1: 4.0580 - dense_1_loss_2: 3.2063 - dense_1_loss_3: 2.2747 - dense_1_loss_4: 1.9115 - dense_1_loss_5: 1.5232 - dense_1_loss_6: 1.2318 - dense_1_loss_7: 1.1687 - dense_1_loss_8: 1.1294 - dense_1_loss_9: 1.0486 - dense_1_loss_10: 0.9668 - dense_1_loss_11: 1.0165 - dense_1_loss_12: 1.0011 - dense_1_loss_13: 0.8906 - dense_1_loss_14: 0.8915 - dense_1_loss_15: 0.9522 - dense_1_loss_16: 0.9425 - dense_1_loss_17: 0.9959 - dense_1_loss_18: 0.9568 - dense_1_loss_19: 0.9452 - dense_1_loss_20: 0.9827 - dense_1_loss_21: 0.9702 - dense_1_loss_22: 0.9839 - dense_1_loss_23: 0.9601 - dense_1_loss_24: 0.9256 - dense_1_loss_25: 1.0594 - dense_1_loss_26: 0.9498 - dense_1_loss_27: 1.0003 - dense_1_loss_28: 0.9784 - dense_1_loss_29: 0.9549 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2833 - dense_1_acc_3: 0.5333 - dense_1_acc_4: 0.4500 - dense_1_acc_5: 0.5500 - dense_1_acc_6: 0.7167 - dense_1_acc_7: 0.8500 - dense_1_acc_8: 0.7167 - dense_1_acc_9: 0.8167 - dense_1_acc_10: 0.8833 - dense_1_acc_11: 0.7667 - dense_1_acc_12: 0.8500 - dense_1_acc_13: 0.9500 - dense_1_acc_14: 0.8833 - dense_1_acc_15: 0.8500 - dense_1_acc_16: 0.9500 - dense_1_acc_17: 0.8333 - dense_1_acc_18: 0.7667 - dense_1_acc_19: 0.8667 - dense_1_acc_20: 0.8333 - dense_1_acc_21: 0.8500 - dense_1_acc_22: 0.9000 - dense_1_acc_23: 0.8833 - dense_1_acc_24: 0.8833 - dense_1_acc_25: 0.7667 - dense_1_acc_26: 0.8833 - dense_1_acc_27: 0.8167 - dense_1_acc_28: 0.8333 - dense_1_acc_29: 0.8833 - dense_1_acc_30: 0.0000e+00     
Epoch 29/100
60/60 [==============================] - 0s - loss: 34.8516 - dense_1_loss_1: 4.0505 - dense_1_loss_2: 3.1603 - dense_1_loss_3: 2.2028 - dense_1_loss_4: 1.8335 - dense_1_loss_5: 1.4395 - dense_1_loss_6: 1.1476 - dense_1_loss_7: 1.0989 - dense_1_loss_8: 1.0629 - dense_1_loss_9: 0.9678 - dense_1_loss_10: 0.9031 - dense_1_loss_11: 0.9193 - dense_1_loss_12: 0.9352 - dense_1_loss_13: 0.8223 - dense_1_loss_14: 0.8405 - dense_1_loss_15: 0.8826 - dense_1_loss_16: 0.8818 - dense_1_loss_17: 0.9120 - dense_1_loss_18: 0.8814 - dense_1_loss_19: 0.8926 - dense_1_loss_20: 0.9129 - dense_1_loss_21: 0.8975 - dense_1_loss_22: 0.9191 - dense_1_loss_23: 0.8582 - dense_1_loss_24: 0.8664 - dense_1_loss_25: 0.9873 - dense_1_loss_26: 0.8851 - dense_1_loss_27: 0.9208 - dense_1_loss_28: 0.8919 - dense_1_loss_29: 0.8781 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2833 - dense_1_acc_3: 0.5333 - dense_1_acc_4: 0.4833 - dense_1_acc_5: 0.5833 - dense_1_acc_6: 0.7667 - dense_1_acc_7: 0.9000 - dense_1_acc_8: 0.7500 - dense_1_acc_9: 0.8000 - dense_1_acc_10: 0.8833 - dense_1_acc_11: 0.8500 - dense_1_acc_12: 0.9167 - dense_1_acc_13: 0.9500 - dense_1_acc_14: 0.9500 - dense_1_acc_15: 0.8833 - dense_1_acc_16: 0.9667 - dense_1_acc_17: 0.9000 - dense_1_acc_18: 0.8833 - dense_1_acc_19: 0.8667 - dense_1_acc_20: 0.9000 - dense_1_acc_21: 0.9333 - dense_1_acc_22: 0.9167 - dense_1_acc_23: 0.9167 - dense_1_acc_24: 0.9167 - dense_1_acc_25: 0.8000 - dense_1_acc_26: 0.9167 - dense_1_acc_27: 0.9000 - dense_1_acc_28: 0.8667 - dense_1_acc_29: 0.8833 - dense_1_acc_30: 0.0000e+00     
Epoch 30/100
60/60 [==============================] - 0s - loss: 33.0396 - dense_1_loss_1: 4.0425 - dense_1_loss_2: 3.1119 - dense_1_loss_3: 2.1338 - dense_1_loss_4: 1.7691 - dense_1_loss_5: 1.3593 - dense_1_loss_6: 1.0798 - dense_1_loss_7: 1.0260 - dense_1_loss_8: 0.9722 - dense_1_loss_9: 0.8939 - dense_1_loss_10: 0.8502 - dense_1_loss_11: 0.8510 - dense_1_loss_12: 0.8728 - dense_1_loss_13: 0.7624 - dense_1_loss_14: 0.8034 - dense_1_loss_15: 0.8181 - dense_1_loss_16: 0.8146 - dense_1_loss_17: 0.8315 - dense_1_loss_18: 0.8346 - dense_1_loss_19: 0.8512 - dense_1_loss_20: 0.8402 - dense_1_loss_21: 0.8208 - dense_1_loss_22: 0.8591 - dense_1_loss_23: 0.7893 - dense_1_loss_24: 0.8128 - dense_1_loss_25: 0.9424 - dense_1_loss_26: 0.7967 - dense_1_loss_27: 0.8467 - dense_1_loss_28: 0.8232 - dense_1_loss_29: 0.8301 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2833 - dense_1_acc_3: 0.5500 - dense_1_acc_4: 0.4833 - dense_1_acc_5: 0.6333 - dense_1_acc_6: 0.7833 - dense_1_acc_7: 0.9500 - dense_1_acc_8: 0.8000 - dense_1_acc_9: 0.8333 - dense_1_acc_10: 0.9000 - dense_1_acc_11: 0.9000 - dense_1_acc_12: 0.9333 - dense_1_acc_13: 0.9667 - dense_1_acc_14: 0.9500 - dense_1_acc_15: 0.8833 - dense_1_acc_16: 0.9667 - dense_1_acc_17: 0.9333 - dense_1_acc_18: 0.9000 - dense_1_acc_19: 0.9000 - dense_1_acc_20: 0.9167 - dense_1_acc_21: 0.9500 - dense_1_acc_22: 0.9333 - dense_1_acc_23: 0.9333 - dense_1_acc_24: 0.9667 - dense_1_acc_25: 0.8333 - dense_1_acc_26: 0.9500 - dense_1_acc_27: 0.9167 - dense_1_acc_28: 0.9000 - dense_1_acc_29: 0.8833 - dense_1_acc_30: 0.0000e+00     
Epoch 31/100
60/60 [==============================] - 0s - loss: 31.2115 - dense_1_loss_1: 4.0347 - dense_1_loss_2: 3.0670 - dense_1_loss_3: 2.0677 - dense_1_loss_4: 1.6894 - dense_1_loss_5: 1.2791 - dense_1_loss_6: 1.0036 - dense_1_loss_7: 0.9697 - dense_1_loss_8: 0.9078 - dense_1_loss_9: 0.8270 - dense_1_loss_10: 0.7842 - dense_1_loss_11: 0.7984 - dense_1_loss_12: 0.7919 - dense_1_loss_13: 0.6998 - dense_1_loss_14: 0.7360 - dense_1_loss_15: 0.7607 - dense_1_loss_16: 0.7495 - dense_1_loss_17: 0.7710 - dense_1_loss_18: 0.7747 - dense_1_loss_19: 0.7774 - dense_1_loss_20: 0.7782 - dense_1_loss_21: 0.7584 - dense_1_loss_22: 0.7907 - dense_1_loss_23: 0.7403 - dense_1_loss_24: 0.7389 - dense_1_loss_25: 0.8684 - dense_1_loss_26: 0.7347 - dense_1_loss_27: 0.7808 - dense_1_loss_28: 0.7643 - dense_1_loss_29: 0.7672 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2833 - dense_1_acc_3: 0.5667 - dense_1_acc_4: 0.4833 - dense_1_acc_5: 0.7000 - dense_1_acc_6: 0.8167 - dense_1_acc_7: 0.9333 - dense_1_acc_8: 0.8667 - dense_1_acc_9: 0.8500 - dense_1_acc_10: 0.9500 - dense_1_acc_11: 0.9000 - dense_1_acc_12: 0.9500 - dense_1_acc_13: 0.9833 - dense_1_acc_14: 0.9500 - dense_1_acc_15: 0.9167 - dense_1_acc_16: 0.9833 - dense_1_acc_17: 0.9333 - dense_1_acc_18: 0.9500 - dense_1_acc_19: 0.9500 - dense_1_acc_20: 0.9500 - dense_1_acc_21: 0.9333 - dense_1_acc_22: 0.9500 - dense_1_acc_23: 0.9833 - dense_1_acc_24: 0.9667 - dense_1_acc_25: 0.8500 - dense_1_acc_26: 0.9500 - dense_1_acc_27: 0.9500 - dense_1_acc_28: 0.9167 - dense_1_acc_29: 0.9000 - dense_1_acc_30: 0.0000e+00     
Epoch 32/100
60/60 [==============================] - 0s - loss: 29.5748 - dense_1_loss_1: 4.0282 - dense_1_loss_2: 3.0201 - dense_1_loss_3: 2.0028 - dense_1_loss_4: 1.6073 - dense_1_loss_5: 1.1976 - dense_1_loss_6: 0.9391 - dense_1_loss_7: 0.8996 - dense_1_loss_8: 0.8674 - dense_1_loss_9: 0.7689 - dense_1_loss_10: 0.7085 - dense_1_loss_11: 0.7296 - dense_1_loss_12: 0.7143 - dense_1_loss_13: 0.6478 - dense_1_loss_14: 0.6798 - dense_1_loss_15: 0.7037 - dense_1_loss_16: 0.6957 - dense_1_loss_17: 0.7155 - dense_1_loss_18: 0.7009 - dense_1_loss_19: 0.7276 - dense_1_loss_20: 0.7203 - dense_1_loss_21: 0.7206 - dense_1_loss_22: 0.7310 - dense_1_loss_23: 0.6957 - dense_1_loss_24: 0.6805 - dense_1_loss_25: 0.8066 - dense_1_loss_26: 0.6915 - dense_1_loss_27: 0.7420 - dense_1_loss_28: 0.7182 - dense_1_loss_29: 0.7141 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2833 - dense_1_acc_3: 0.5500 - dense_1_acc_4: 0.5500 - dense_1_acc_5: 0.7333 - dense_1_acc_6: 0.8000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.8167 - dense_1_acc_9: 0.8833 - dense_1_acc_10: 0.9167 - dense_1_acc_11: 0.9667 - dense_1_acc_12: 0.9667 - dense_1_acc_13: 0.9833 - dense_1_acc_14: 0.9500 - dense_1_acc_15: 0.9500 - dense_1_acc_16: 0.9833 - dense_1_acc_17: 0.9500 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9667 - dense_1_acc_20: 0.9667 - dense_1_acc_21: 0.9667 - dense_1_acc_22: 0.9833 - dense_1_acc_23: 0.9833 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.8667 - dense_1_acc_26: 0.9667 - dense_1_acc_27: 0.9500 - dense_1_acc_28: 0.9500 - dense_1_acc_29: 0.9000 - dense_1_acc_30: 0.0000e+00     
Epoch 33/100
60/60 [==============================] - 0s - loss: 27.9737 - dense_1_loss_1: 4.0216 - dense_1_loss_2: 2.9721 - dense_1_loss_3: 1.9386 - dense_1_loss_4: 1.5331 - dense_1_loss_5: 1.1200 - dense_1_loss_6: 0.8744 - dense_1_loss_7: 0.8335 - dense_1_loss_8: 0.7904 - dense_1_loss_9: 0.7247 - dense_1_loss_10: 0.6550 - dense_1_loss_11: 0.6820 - dense_1_loss_12: 0.6602 - dense_1_loss_13: 0.6051 - dense_1_loss_14: 0.6329 - dense_1_loss_15: 0.6448 - dense_1_loss_16: 0.6574 - dense_1_loss_17: 0.6564 - dense_1_loss_18: 0.6470 - dense_1_loss_19: 0.6808 - dense_1_loss_20: 0.6682 - dense_1_loss_21: 0.6471 - dense_1_loss_22: 0.6879 - dense_1_loss_23: 0.6490 - dense_1_loss_24: 0.6138 - dense_1_loss_25: 0.7459 - dense_1_loss_26: 0.6281 - dense_1_loss_27: 0.6810 - dense_1_loss_28: 0.6606 - dense_1_loss_29: 0.6623 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.5500 - dense_1_acc_4: 0.5667 - dense_1_acc_5: 0.7500 - dense_1_acc_6: 0.8333 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9167 - dense_1_acc_9: 0.9000 - dense_1_acc_10: 0.9833 - dense_1_acc_11: 0.9167 - dense_1_acc_12: 0.9833 - dense_1_acc_13: 0.9833 - dense_1_acc_14: 0.9667 - dense_1_acc_15: 0.9667 - dense_1_acc_16: 0.9833 - dense_1_acc_17: 0.9667 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9667 - dense_1_acc_20: 0.9833 - dense_1_acc_21: 0.9833 - dense_1_acc_22: 0.9833 - dense_1_acc_23: 0.9833 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9000 - dense_1_acc_30: 0.0000e+00     
Epoch 34/100
60/60 [==============================] - 0s - loss: 26.4537 - dense_1_loss_1: 4.0143 - dense_1_loss_2: 2.9284 - dense_1_loss_3: 1.8764 - dense_1_loss_4: 1.4577 - dense_1_loss_5: 1.0544 - dense_1_loss_6: 0.8088 - dense_1_loss_7: 0.7924 - dense_1_loss_8: 0.7171 - dense_1_loss_9: 0.6815 - dense_1_loss_10: 0.6032 - dense_1_loss_11: 0.6289 - dense_1_loss_12: 0.6122 - dense_1_loss_13: 0.5585 - dense_1_loss_14: 0.5847 - dense_1_loss_15: 0.6003 - dense_1_loss_16: 0.5864 - dense_1_loss_17: 0.6140 - dense_1_loss_18: 0.6007 - dense_1_loss_19: 0.6066 - dense_1_loss_20: 0.6227 - dense_1_loss_21: 0.6074 - dense_1_loss_22: 0.6242 - dense_1_loss_23: 0.5913 - dense_1_loss_24: 0.5784 - dense_1_loss_25: 0.6959 - dense_1_loss_26: 0.5685 - dense_1_loss_27: 0.6152 - dense_1_loss_28: 0.6178 - dense_1_loss_29: 0.6057 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.5667 - dense_1_acc_4: 0.6167 - dense_1_acc_5: 0.7833 - dense_1_acc_6: 0.8333 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9167 - dense_1_acc_9: 0.9167 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9667 - dense_1_acc_12: 0.9667 - dense_1_acc_13: 0.9500 - dense_1_acc_14: 0.9833 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 0.9667 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9833 - dense_1_acc_20: 0.9833 - dense_1_acc_21: 0.9833 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 0.9833 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9333 - dense_1_acc_30: 0.0000e+00     
Epoch 35/100
60/60 [==============================] - 0s - loss: 25.3020 - dense_1_loss_1: 4.0076 - dense_1_loss_2: 2.8809 - dense_1_loss_3: 1.8127 - dense_1_loss_4: 1.3818 - dense_1_loss_5: 0.9883 - dense_1_loss_6: 0.7545 - dense_1_loss_7: 0.7398 - dense_1_loss_8: 0.6632 - dense_1_loss_9: 0.6211 - dense_1_loss_10: 0.5547 - dense_1_loss_11: 0.5828 - dense_1_loss_12: 0.5680 - dense_1_loss_13: 0.5074 - dense_1_loss_14: 0.5488 - dense_1_loss_15: 0.5518 - dense_1_loss_16: 0.5706 - dense_1_loss_17: 0.5637 - dense_1_loss_18: 0.5603 - dense_1_loss_19: 0.5770 - dense_1_loss_20: 0.5882 - dense_1_loss_21: 0.5975 - dense_1_loss_22: 0.5684 - dense_1_loss_23: 0.5613 - dense_1_loss_24: 0.5658 - dense_1_loss_25: 0.6647 - dense_1_loss_26: 0.5433 - dense_1_loss_27: 0.6020 - dense_1_loss_28: 0.5876 - dense_1_loss_29: 0.5882 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3500 - dense_1_acc_3: 0.6000 - dense_1_acc_4: 0.6500 - dense_1_acc_5: 0.7833 - dense_1_acc_6: 0.8667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9500 - dense_1_acc_9: 0.9000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9667 - dense_1_acc_12: 0.9833 - dense_1_acc_13: 0.9833 - dense_1_acc_14: 0.9667 - dense_1_acc_15: 0.9833 - dense_1_acc_16: 0.9833 - dense_1_acc_17: 0.9833 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9667 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 0.9833 - dense_1_acc_22: 0.9833 - dense_1_acc_23: 0.9833 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.8833 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 0.9833 - dense_1_acc_28: 0.9667 - dense_1_acc_29: 0.9000 - dense_1_acc_30: 0.0000e+00     
Epoch 36/100
60/60 [==============================] - 0s - loss: 24.0212 - dense_1_loss_1: 4.0018 - dense_1_loss_2: 2.8350 - dense_1_loss_3: 1.7567 - dense_1_loss_4: 1.3129 - dense_1_loss_5: 0.9261 - dense_1_loss_6: 0.7048 - dense_1_loss_7: 0.7056 - dense_1_loss_8: 0.5899 - dense_1_loss_9: 0.5723 - dense_1_loss_10: 0.5290 - dense_1_loss_11: 0.5672 - dense_1_loss_12: 0.5433 - dense_1_loss_13: 0.4691 - dense_1_loss_14: 0.5052 - dense_1_loss_15: 0.5154 - dense_1_loss_16: 0.5191 - dense_1_loss_17: 0.5147 - dense_1_loss_18: 0.5275 - dense_1_loss_19: 0.5322 - dense_1_loss_20: 0.5460 - dense_1_loss_21: 0.5364 - dense_1_loss_22: 0.5149 - dense_1_loss_23: 0.5171 - dense_1_loss_24: 0.5470 - dense_1_loss_25: 0.6014 - dense_1_loss_26: 0.5223 - dense_1_loss_27: 0.5298 - dense_1_loss_28: 0.5323 - dense_1_loss_29: 0.5465 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.6167 - dense_1_acc_4: 0.6500 - dense_1_acc_5: 0.8000 - dense_1_acc_6: 0.8833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9500 - dense_1_acc_9: 0.9333 - dense_1_acc_10: 0.9833 - dense_1_acc_11: 0.9333 - dense_1_acc_12: 0.9667 - dense_1_acc_13: 0.9833 - dense_1_acc_14: 0.9833 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 0.9833 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 0.9833 - dense_1_acc_19: 0.9833 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 0.9833 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9333 - dense_1_acc_26: 0.9667 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9333 - dense_1_acc_30: 0.0000e+00     
Epoch 37/100
60/60 [==============================] - 0s - loss: 22.7911 - dense_1_loss_1: 3.9958 - dense_1_loss_2: 2.7889 - dense_1_loss_3: 1.6957 - dense_1_loss_4: 1.2449 - dense_1_loss_5: 0.8664 - dense_1_loss_6: 0.6546 - dense_1_loss_7: 0.6433 - dense_1_loss_8: 0.5611 - dense_1_loss_9: 0.5342 - dense_1_loss_10: 0.4752 - dense_1_loss_11: 0.4942 - dense_1_loss_12: 0.4948 - dense_1_loss_13: 0.4327 - dense_1_loss_14: 0.4618 - dense_1_loss_15: 0.4805 - dense_1_loss_16: 0.4725 - dense_1_loss_17: 0.4893 - dense_1_loss_18: 0.4711 - dense_1_loss_19: 0.5226 - dense_1_loss_20: 0.4978 - dense_1_loss_21: 0.4973 - dense_1_loss_22: 0.5085 - dense_1_loss_23: 0.4918 - dense_1_loss_24: 0.4780 - dense_1_loss_25: 0.5687 - dense_1_loss_26: 0.5094 - dense_1_loss_27: 0.4872 - dense_1_loss_28: 0.4759 - dense_1_loss_29: 0.4969 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.6167 - dense_1_acc_4: 0.6833 - dense_1_acc_5: 0.8333 - dense_1_acc_6: 0.9167 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9667 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9667 - dense_1_acc_12: 0.9833 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 0.9833 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 0.9833 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9833 - dense_1_acc_20: 0.9667 - dense_1_acc_21: 0.9500 - dense_1_acc_22: 0.9833 - dense_1_acc_23: 0.9833 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9333 - dense_1_acc_26: 0.9500 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 38/100
60/60 [==============================] - 0s - loss: 21.5434 - dense_1_loss_1: 3.9901 - dense_1_loss_2: 2.7436 - dense_1_loss_3: 1.6372 - dense_1_loss_4: 1.1774 - dense_1_loss_5: 0.8135 - dense_1_loss_6: 0.6036 - dense_1_loss_7: 0.5891 - dense_1_loss_8: 0.5241 - dense_1_loss_9: 0.4945 - dense_1_loss_10: 0.4275 - dense_1_loss_11: 0.4470 - dense_1_loss_12: 0.4405 - dense_1_loss_13: 0.3920 - dense_1_loss_14: 0.4135 - dense_1_loss_15: 0.4474 - dense_1_loss_16: 0.4103 - dense_1_loss_17: 0.4569 - dense_1_loss_18: 0.4348 - dense_1_loss_19: 0.4566 - dense_1_loss_20: 0.4556 - dense_1_loss_21: 0.4633 - dense_1_loss_22: 0.4516 - dense_1_loss_23: 0.4803 - dense_1_loss_24: 0.4165 - dense_1_loss_25: 0.5328 - dense_1_loss_26: 0.4615 - dense_1_loss_27: 0.4715 - dense_1_loss_28: 0.4605 - dense_1_loss_29: 0.4501 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.6167 - dense_1_acc_4: 0.7500 - dense_1_acc_5: 0.9000 - dense_1_acc_6: 0.9333 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 0.9833 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 0.9833 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9667 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 39/100
60/60 [==============================] - 0s - loss: 20.4096 - dense_1_loss_1: 3.9847 - dense_1_loss_2: 2.6981 - dense_1_loss_3: 1.5830 - dense_1_loss_4: 1.1062 - dense_1_loss_5: 0.7642 - dense_1_loss_6: 0.5615 - dense_1_loss_7: 0.5547 - dense_1_loss_8: 0.4929 - dense_1_loss_9: 0.4626 - dense_1_loss_10: 0.3892 - dense_1_loss_11: 0.4192 - dense_1_loss_12: 0.4008 - dense_1_loss_13: 0.3720 - dense_1_loss_14: 0.3958 - dense_1_loss_15: 0.4000 - dense_1_loss_16: 0.3839 - dense_1_loss_17: 0.4178 - dense_1_loss_18: 0.4045 - dense_1_loss_19: 0.4057 - dense_1_loss_20: 0.4201 - dense_1_loss_21: 0.4235 - dense_1_loss_22: 0.3947 - dense_1_loss_23: 0.4373 - dense_1_loss_24: 0.3695 - dense_1_loss_25: 0.4961 - dense_1_loss_26: 0.4024 - dense_1_loss_27: 0.4213 - dense_1_loss_28: 0.4212 - dense_1_loss_29: 0.4268 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.6167 - dense_1_acc_4: 0.7667 - dense_1_acc_5: 0.9000 - dense_1_acc_6: 0.9500 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9833 - dense_1_acc_12: 0.9833 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 0.9833 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 40/100
60/60 [==============================] - 0s - loss: 19.3388 - dense_1_loss_1: 3.9784 - dense_1_loss_2: 2.6535 - dense_1_loss_3: 1.5344 - dense_1_loss_4: 1.0452 - dense_1_loss_5: 0.7121 - dense_1_loss_6: 0.5257 - dense_1_loss_7: 0.5184 - dense_1_loss_8: 0.4365 - dense_1_loss_9: 0.4269 - dense_1_loss_10: 0.3572 - dense_1_loss_11: 0.3919 - dense_1_loss_12: 0.3681 - dense_1_loss_13: 0.3411 - dense_1_loss_14: 0.3671 - dense_1_loss_15: 0.3574 - dense_1_loss_16: 0.3562 - dense_1_loss_17: 0.3790 - dense_1_loss_18: 0.3677 - dense_1_loss_19: 0.3747 - dense_1_loss_20: 0.3929 - dense_1_loss_21: 0.3757 - dense_1_loss_22: 0.3625 - dense_1_loss_23: 0.3857 - dense_1_loss_24: 0.3530 - dense_1_loss_25: 0.4471 - dense_1_loss_26: 0.3708 - dense_1_loss_27: 0.3588 - dense_1_loss_28: 0.3926 - dense_1_loss_29: 0.4082 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3667 - dense_1_acc_3: 0.6167 - dense_1_acc_4: 0.7833 - dense_1_acc_5: 0.9000 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 0.9833 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 41/100
60/60 [==============================] - 0s - loss: 18.4427 - dense_1_loss_1: 3.9727 - dense_1_loss_2: 2.6086 - dense_1_loss_3: 1.4855 - dense_1_loss_4: 0.9911 - dense_1_loss_5: 0.6693 - dense_1_loss_6: 0.4911 - dense_1_loss_7: 0.4898 - dense_1_loss_8: 0.3894 - dense_1_loss_9: 0.3934 - dense_1_loss_10: 0.3294 - dense_1_loss_11: 0.3655 - dense_1_loss_12: 0.3469 - dense_1_loss_13: 0.3107 - dense_1_loss_14: 0.3240 - dense_1_loss_15: 0.3409 - dense_1_loss_16: 0.3289 - dense_1_loss_17: 0.3514 - dense_1_loss_18: 0.3306 - dense_1_loss_19: 0.3551 - dense_1_loss_20: 0.3632 - dense_1_loss_21: 0.3502 - dense_1_loss_22: 0.3416 - dense_1_loss_23: 0.3415 - dense_1_loss_24: 0.3464 - dense_1_loss_25: 0.4083 - dense_1_loss_26: 0.3446 - dense_1_loss_27: 0.3314 - dense_1_loss_28: 0.3591 - dense_1_loss_29: 0.3821 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.6333 - dense_1_acc_4: 0.7833 - dense_1_acc_5: 0.9167 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 0.9833 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 0.9833 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 42/100
60/60 [==============================] - 0s - loss: 17.5646 - dense_1_loss_1: 3.9672 - dense_1_loss_2: 2.5679 - dense_1_loss_3: 1.4368 - dense_1_loss_4: 0.9286 - dense_1_loss_5: 0.6270 - dense_1_loss_6: 0.4565 - dense_1_loss_7: 0.4589 - dense_1_loss_8: 0.3703 - dense_1_loss_9: 0.3668 - dense_1_loss_10: 0.3025 - dense_1_loss_11: 0.3322 - dense_1_loss_12: 0.3195 - dense_1_loss_13: 0.2819 - dense_1_loss_14: 0.2898 - dense_1_loss_15: 0.3175 - dense_1_loss_16: 0.3034 - dense_1_loss_17: 0.3205 - dense_1_loss_18: 0.3035 - dense_1_loss_19: 0.3269 - dense_1_loss_20: 0.3342 - dense_1_loss_21: 0.3339 - dense_1_loss_22: 0.3222 - dense_1_loss_23: 0.3123 - dense_1_loss_24: 0.3104 - dense_1_loss_25: 0.3722 - dense_1_loss_26: 0.3139 - dense_1_loss_27: 0.3122 - dense_1_loss_28: 0.3210 - dense_1_loss_29: 0.3546 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.6333 - dense_1_acc_4: 0.8167 - dense_1_acc_5: 0.9333 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 43/100
60/60 [==============================] - 0s - loss: 16.7271 - dense_1_loss_1: 3.9624 - dense_1_loss_2: 2.5229 - dense_1_loss_3: 1.3891 - dense_1_loss_4: 0.8766 - dense_1_loss_5: 0.5865 - dense_1_loss_6: 0.4224 - dense_1_loss_7: 0.4348 - dense_1_loss_8: 0.3382 - dense_1_loss_9: 0.3413 - dense_1_loss_10: 0.2778 - dense_1_loss_11: 0.3062 - dense_1_loss_12: 0.2913 - dense_1_loss_13: 0.2632 - dense_1_loss_14: 0.2670 - dense_1_loss_15: 0.2825 - dense_1_loss_16: 0.2767 - dense_1_loss_17: 0.2871 - dense_1_loss_18: 0.2843 - dense_1_loss_19: 0.2982 - dense_1_loss_20: 0.2984 - dense_1_loss_21: 0.3035 - dense_1_loss_22: 0.2955 - dense_1_loss_23: 0.2923 - dense_1_loss_24: 0.2757 - dense_1_loss_25: 0.3403 - dense_1_loss_26: 0.2941 - dense_1_loss_27: 0.2997 - dense_1_loss_28: 0.2961 - dense_1_loss_29: 0.3230 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.8500 - dense_1_acc_5: 0.9333 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 44/100
60/60 [==============================] - 0s - loss: 16.0275 - dense_1_loss_1: 3.9571 - dense_1_loss_2: 2.4818 - dense_1_loss_3: 1.3443 - dense_1_loss_4: 0.8290 - dense_1_loss_5: 0.5499 - dense_1_loss_6: 0.3926 - dense_1_loss_7: 0.4075 - dense_1_loss_8: 0.3038 - dense_1_loss_9: 0.3181 - dense_1_loss_10: 0.2565 - dense_1_loss_11: 0.2853 - dense_1_loss_12: 0.2629 - dense_1_loss_13: 0.2446 - dense_1_loss_14: 0.2522 - dense_1_loss_15: 0.2562 - dense_1_loss_16: 0.2535 - dense_1_loss_17: 0.2654 - dense_1_loss_18: 0.2687 - dense_1_loss_19: 0.2804 - dense_1_loss_20: 0.2723 - dense_1_loss_21: 0.2774 - dense_1_loss_22: 0.2747 - dense_1_loss_23: 0.2851 - dense_1_loss_24: 0.2579 - dense_1_loss_25: 0.3207 - dense_1_loss_26: 0.2677 - dense_1_loss_27: 0.2866 - dense_1_loss_28: 0.2823 - dense_1_loss_29: 0.2931 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.8667 - dense_1_acc_5: 0.9333 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 45/100
60/60 [==============================] - 0s - loss: 15.2892 - dense_1_loss_1: 3.9528 - dense_1_loss_2: 2.4409 - dense_1_loss_3: 1.2989 - dense_1_loss_4: 0.7792 - dense_1_loss_5: 0.5144 - dense_1_loss_6: 0.3656 - dense_1_loss_7: 0.3803 - dense_1_loss_8: 0.2880 - dense_1_loss_9: 0.2896 - dense_1_loss_10: 0.2389 - dense_1_loss_11: 0.2588 - dense_1_loss_12: 0.2401 - dense_1_loss_13: 0.2234 - dense_1_loss_14: 0.2316 - dense_1_loss_15: 0.2370 - dense_1_loss_16: 0.2391 - dense_1_loss_17: 0.2397 - dense_1_loss_18: 0.2462 - dense_1_loss_19: 0.2602 - dense_1_loss_20: 0.2470 - dense_1_loss_21: 0.2554 - dense_1_loss_22: 0.2459 - dense_1_loss_23: 0.2618 - dense_1_loss_24: 0.2310 - dense_1_loss_25: 0.2938 - dense_1_loss_26: 0.2389 - dense_1_loss_27: 0.2560 - dense_1_loss_28: 0.2606 - dense_1_loss_29: 0.2741 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.8667 - dense_1_acc_5: 0.9500 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 46/100
60/60 [==============================] - 0s - loss: 14.6790 - dense_1_loss_1: 3.9472 - dense_1_loss_2: 2.3999 - dense_1_loss_3: 1.2582 - dense_1_loss_4: 0.7286 - dense_1_loss_5: 0.4830 - dense_1_loss_6: 0.3464 - dense_1_loss_7: 0.3574 - dense_1_loss_8: 0.2724 - dense_1_loss_9: 0.2679 - dense_1_loss_10: 0.2224 - dense_1_loss_11: 0.2386 - dense_1_loss_12: 0.2260 - dense_1_loss_13: 0.2022 - dense_1_loss_14: 0.2122 - dense_1_loss_15: 0.2253 - dense_1_loss_16: 0.2226 - dense_1_loss_17: 0.2267 - dense_1_loss_18: 0.2232 - dense_1_loss_19: 0.2375 - dense_1_loss_20: 0.2378 - dense_1_loss_21: 0.2357 - dense_1_loss_22: 0.2314 - dense_1_loss_23: 0.2384 - dense_1_loss_24: 0.2123 - dense_1_loss_25: 0.2717 - dense_1_loss_26: 0.2229 - dense_1_loss_27: 0.2288 - dense_1_loss_28: 0.2388 - dense_1_loss_29: 0.2635 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4333 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.8667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 47/100
60/60 [==============================] - 0s - loss: 14.0970 - dense_1_loss_1: 3.9425 - dense_1_loss_2: 2.3600 - dense_1_loss_3: 1.2195 - dense_1_loss_4: 0.6886 - dense_1_loss_5: 0.4519 - dense_1_loss_6: 0.3266 - dense_1_loss_7: 0.3332 - dense_1_loss_8: 0.2453 - dense_1_loss_9: 0.2445 - dense_1_loss_10: 0.2051 - dense_1_loss_11: 0.2236 - dense_1_loss_12: 0.2130 - dense_1_loss_13: 0.1839 - dense_1_loss_14: 0.1961 - dense_1_loss_15: 0.2109 - dense_1_loss_16: 0.2001 - dense_1_loss_17: 0.2142 - dense_1_loss_18: 0.2039 - dense_1_loss_19: 0.2189 - dense_1_loss_20: 0.2237 - dense_1_loss_21: 0.2180 - dense_1_loss_22: 0.2095 - dense_1_loss_23: 0.2203 - dense_1_loss_24: 0.2009 - dense_1_loss_25: 0.2488 - dense_1_loss_26: 0.2097 - dense_1_loss_27: 0.2100 - dense_1_loss_28: 0.2275 - dense_1_loss_29: 0.2467 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4500 - dense_1_acc_3: 0.6667 - dense_1_acc_4: 0.8667 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 48/100
60/60 [==============================] - 0s - loss: 13.5650 - dense_1_loss_1: 3.9380 - dense_1_loss_2: 2.3213 - dense_1_loss_3: 1.1828 - dense_1_loss_4: 0.6479 - dense_1_loss_5: 0.4232 - dense_1_loss_6: 0.3065 - dense_1_loss_7: 0.3102 - dense_1_loss_8: 0.2231 - dense_1_loss_9: 0.2256 - dense_1_loss_10: 0.1906 - dense_1_loss_11: 0.2100 - dense_1_loss_12: 0.1964 - dense_1_loss_13: 0.1716 - dense_1_loss_14: 0.1827 - dense_1_loss_15: 0.1923 - dense_1_loss_16: 0.1898 - dense_1_loss_17: 0.1986 - dense_1_loss_18: 0.1855 - dense_1_loss_19: 0.2080 - dense_1_loss_20: 0.2070 - dense_1_loss_21: 0.1993 - dense_1_loss_22: 0.1943 - dense_1_loss_23: 0.2010 - dense_1_loss_24: 0.1894 - dense_1_loss_25: 0.2308 - dense_1_loss_26: 0.1998 - dense_1_loss_27: 0.2002 - dense_1_loss_28: 0.2135 - dense_1_loss_29: 0.2255 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4667 - dense_1_acc_3: 0.6833 - dense_1_acc_4: 0.8667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 49/100
60/60 [==============================] - 0s - loss: 13.0597 - dense_1_loss_1: 3.9339 - dense_1_loss_2: 2.2832 - dense_1_loss_3: 1.1444 - dense_1_loss_4: 0.6110 - dense_1_loss_5: 0.3977 - dense_1_loss_6: 0.2916 - dense_1_loss_7: 0.2918 - dense_1_loss_8: 0.2103 - dense_1_loss_9: 0.2145 - dense_1_loss_10: 0.1770 - dense_1_loss_11: 0.1955 - dense_1_loss_12: 0.1818 - dense_1_loss_13: 0.1640 - dense_1_loss_14: 0.1702 - dense_1_loss_15: 0.1761 - dense_1_loss_16: 0.1776 - dense_1_loss_17: 0.1823 - dense_1_loss_18: 0.1722 - dense_1_loss_19: 0.1921 - dense_1_loss_20: 0.1922 - dense_1_loss_21: 0.1825 - dense_1_loss_22: 0.1838 - dense_1_loss_23: 0.1810 - dense_1_loss_24: 0.1717 - dense_1_loss_25: 0.2133 - dense_1_loss_26: 0.1796 - dense_1_loss_27: 0.1877 - dense_1_loss_28: 0.1932 - dense_1_loss_29: 0.2074 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4667 - dense_1_acc_3: 0.7167 - dense_1_acc_4: 0.8667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 50/100
60/60 [==============================] - 0s - loss: 12.6026 - dense_1_loss_1: 3.9298 - dense_1_loss_2: 2.2459 - dense_1_loss_3: 1.1076 - dense_1_loss_4: 0.5701 - dense_1_loss_5: 0.3745 - dense_1_loss_6: 0.2743 - dense_1_loss_7: 0.2741 - dense_1_loss_8: 0.1958 - dense_1_loss_9: 0.2036 - dense_1_loss_10: 0.1632 - dense_1_loss_11: 0.1818 - dense_1_loss_12: 0.1658 - dense_1_loss_13: 0.1562 - dense_1_loss_14: 0.1593 - dense_1_loss_15: 0.1637 - dense_1_loss_16: 0.1626 - dense_1_loss_17: 0.1675 - dense_1_loss_18: 0.1637 - dense_1_loss_19: 0.1737 - dense_1_loss_20: 0.1801 - dense_1_loss_21: 0.1730 - dense_1_loss_22: 0.1703 - dense_1_loss_23: 0.1721 - dense_1_loss_24: 0.1558 - dense_1_loss_25: 0.1999 - dense_1_loss_26: 0.1673 - dense_1_loss_27: 0.1757 - dense_1_loss_28: 0.1815 - dense_1_loss_29: 0.1938 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4667 - dense_1_acc_3: 0.7167 - dense_1_acc_4: 0.9333 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 51/100
60/60 [==============================] - 0s - loss: 12.1969 - dense_1_loss_1: 3.9255 - dense_1_loss_2: 2.2080 - dense_1_loss_3: 1.0726 - dense_1_loss_4: 0.5390 - dense_1_loss_5: 0.3548 - dense_1_loss_6: 0.2577 - dense_1_loss_7: 0.2564 - dense_1_loss_8: 0.1868 - dense_1_loss_9: 0.1882 - dense_1_loss_10: 0.1529 - dense_1_loss_11: 0.1652 - dense_1_loss_12: 0.1568 - dense_1_loss_13: 0.1446 - dense_1_loss_14: 0.1448 - dense_1_loss_15: 0.1528 - dense_1_loss_16: 0.1549 - dense_1_loss_17: 0.1573 - dense_1_loss_18: 0.1558 - dense_1_loss_19: 0.1594 - dense_1_loss_20: 0.1655 - dense_1_loss_21: 0.1656 - dense_1_loss_22: 0.1577 - dense_1_loss_23: 0.1639 - dense_1_loss_24: 0.1475 - dense_1_loss_25: 0.1874 - dense_1_loss_26: 0.1574 - dense_1_loss_27: 0.1644 - dense_1_loss_28: 0.1717 - dense_1_loss_29: 0.1822 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4667 - dense_1_acc_3: 0.7333 - dense_1_acc_4: 0.9333 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 52/100
60/60 [==============================] - 0s - loss: 11.7999 - dense_1_loss_1: 3.9217 - dense_1_loss_2: 2.1720 - dense_1_loss_3: 1.0392 - dense_1_loss_4: 0.5107 - dense_1_loss_5: 0.3348 - dense_1_loss_6: 0.2429 - dense_1_loss_7: 0.2425 - dense_1_loss_8: 0.1760 - dense_1_loss_9: 0.1731 - dense_1_loss_10: 0.1442 - dense_1_loss_11: 0.1543 - dense_1_loss_12: 0.1459 - dense_1_loss_13: 0.1337 - dense_1_loss_14: 0.1360 - dense_1_loss_15: 0.1401 - dense_1_loss_16: 0.1463 - dense_1_loss_17: 0.1466 - dense_1_loss_18: 0.1444 - dense_1_loss_19: 0.1508 - dense_1_loss_20: 0.1517 - dense_1_loss_21: 0.1525 - dense_1_loss_22: 0.1483 - dense_1_loss_23: 0.1505 - dense_1_loss_24: 0.1387 - dense_1_loss_25: 0.1725 - dense_1_loss_26: 0.1485 - dense_1_loss_27: 0.1516 - dense_1_loss_28: 0.1598 - dense_1_loss_29: 0.1704 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4667 - dense_1_acc_3: 0.7333 - dense_1_acc_4: 0.9333 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 53/100
60/60 [==============================] - 0s - loss: 11.4567 - dense_1_loss_1: 3.9172 - dense_1_loss_2: 2.1361 - dense_1_loss_3: 1.0079 - dense_1_loss_4: 0.4832 - dense_1_loss_5: 0.3156 - dense_1_loss_6: 0.2311 - dense_1_loss_7: 0.2329 - dense_1_loss_8: 0.1637 - dense_1_loss_9: 0.1621 - dense_1_loss_10: 0.1354 - dense_1_loss_11: 0.1477 - dense_1_loss_12: 0.1379 - dense_1_loss_13: 0.1248 - dense_1_loss_14: 0.1295 - dense_1_loss_15: 0.1316 - dense_1_loss_16: 0.1364 - dense_1_loss_17: 0.1378 - dense_1_loss_18: 0.1345 - dense_1_loss_19: 0.1434 - dense_1_loss_20: 0.1433 - dense_1_loss_21: 0.1424 - dense_1_loss_22: 0.1385 - dense_1_loss_23: 0.1395 - dense_1_loss_24: 0.1307 - dense_1_loss_25: 0.1613 - dense_1_loss_26: 0.1397 - dense_1_loss_27: 0.1392 - dense_1_loss_28: 0.1497 - dense_1_loss_29: 0.1637 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4500 - dense_1_acc_3: 0.7833 - dense_1_acc_4: 0.9333 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 54/100
60/60 [==============================] - 0s - loss: 11.1297 - dense_1_loss_1: 3.9131 - dense_1_loss_2: 2.1021 - dense_1_loss_3: 0.9777 - dense_1_loss_4: 0.4565 - dense_1_loss_5: 0.2980 - dense_1_loss_6: 0.2192 - dense_1_loss_7: 0.2220 - dense_1_loss_8: 0.1525 - dense_1_loss_9: 0.1522 - dense_1_loss_10: 0.1263 - dense_1_loss_11: 0.1413 - dense_1_loss_12: 0.1293 - dense_1_loss_13: 0.1164 - dense_1_loss_14: 0.1224 - dense_1_loss_15: 0.1257 - dense_1_loss_16: 0.1262 - dense_1_loss_17: 0.1297 - dense_1_loss_18: 0.1259 - dense_1_loss_19: 0.1343 - dense_1_loss_20: 0.1374 - dense_1_loss_21: 0.1325 - dense_1_loss_22: 0.1280 - dense_1_loss_23: 0.1302 - dense_1_loss_24: 0.1229 - dense_1_loss_25: 0.1515 - dense_1_loss_26: 0.1306 - dense_1_loss_27: 0.1295 - dense_1_loss_28: 0.1410 - dense_1_loss_29: 0.1554 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.7833 - dense_1_acc_4: 0.9333 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 55/100
60/60 [==============================] - 0s - loss: 10.8305 - dense_1_loss_1: 3.9091 - dense_1_loss_2: 2.0676 - dense_1_loss_3: 0.9481 - dense_1_loss_4: 0.4333 - dense_1_loss_5: 0.2845 - dense_1_loss_6: 0.2080 - dense_1_loss_7: 0.2110 - dense_1_loss_8: 0.1461 - dense_1_loss_9: 0.1429 - dense_1_loss_10: 0.1194 - dense_1_loss_11: 0.1314 - dense_1_loss_12: 0.1221 - dense_1_loss_13: 0.1087 - dense_1_loss_14: 0.1134 - dense_1_loss_15: 0.1198 - dense_1_loss_16: 0.1214 - dense_1_loss_17: 0.1213 - dense_1_loss_18: 0.1185 - dense_1_loss_19: 0.1254 - dense_1_loss_20: 0.1298 - dense_1_loss_21: 0.1259 - dense_1_loss_22: 0.1191 - dense_1_loss_23: 0.1219 - dense_1_loss_24: 0.1151 - dense_1_loss_25: 0.1430 - dense_1_loss_26: 0.1220 - dense_1_loss_27: 0.1234 - dense_1_loss_28: 0.1338 - dense_1_loss_29: 0.1443 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.8000 - dense_1_acc_4: 0.9333 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 56/100
60/60 [==============================] - 0s - loss: 10.5476 - dense_1_loss_1: 3.9054 - dense_1_loss_2: 2.0361 - dense_1_loss_3: 0.9192 - dense_1_loss_4: 0.4097 - dense_1_loss_5: 0.2699 - dense_1_loss_6: 0.1966 - dense_1_loss_7: 0.1976 - dense_1_loss_8: 0.1380 - dense_1_loss_9: 0.1355 - dense_1_loss_10: 0.1130 - dense_1_loss_11: 0.1203 - dense_1_loss_12: 0.1168 - dense_1_loss_13: 0.1029 - dense_1_loss_14: 0.1052 - dense_1_loss_15: 0.1129 - dense_1_loss_16: 0.1181 - dense_1_loss_17: 0.1149 - dense_1_loss_18: 0.1110 - dense_1_loss_19: 0.1188 - dense_1_loss_20: 0.1210 - dense_1_loss_21: 0.1195 - dense_1_loss_22: 0.1123 - dense_1_loss_23: 0.1150 - dense_1_loss_24: 0.1095 - dense_1_loss_25: 0.1334 - dense_1_loss_26: 0.1156 - dense_1_loss_27: 0.1183 - dense_1_loss_28: 0.1270 - dense_1_loss_29: 0.1341 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.8333 - dense_1_acc_4: 0.9333 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 57/100
60/60 [==============================] - 0s - loss: 10.2853 - dense_1_loss_1: 3.9014 - dense_1_loss_2: 2.0034 - dense_1_loss_3: 0.8925 - dense_1_loss_4: 0.3899 - dense_1_loss_5: 0.2565 - dense_1_loss_6: 0.1892 - dense_1_loss_7: 0.1875 - dense_1_loss_8: 0.1288 - dense_1_loss_9: 0.1290 - dense_1_loss_10: 0.1055 - dense_1_loss_11: 0.1154 - dense_1_loss_12: 0.1096 - dense_1_loss_13: 0.0982 - dense_1_loss_14: 0.1008 - dense_1_loss_15: 0.1044 - dense_1_loss_16: 0.1075 - dense_1_loss_17: 0.1089 - dense_1_loss_18: 0.1047 - dense_1_loss_19: 0.1123 - dense_1_loss_20: 0.1138 - dense_1_loss_21: 0.1113 - dense_1_loss_22: 0.1068 - dense_1_loss_23: 0.1086 - dense_1_loss_24: 0.1038 - dense_1_loss_25: 0.1242 - dense_1_loss_26: 0.1097 - dense_1_loss_27: 0.1119 - dense_1_loss_28: 0.1217 - dense_1_loss_29: 0.1279 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.8667 - dense_1_acc_4: 0.9333 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 58/100
60/60 [==============================] - 0s - loss: 10.0500 - dense_1_loss_1: 3.8974 - dense_1_loss_2: 1.9716 - dense_1_loss_3: 0.8691 - dense_1_loss_4: 0.3699 - dense_1_loss_5: 0.2441 - dense_1_loss_6: 0.1818 - dense_1_loss_7: 0.1776 - dense_1_loss_8: 0.1225 - dense_1_loss_9: 0.1224 - dense_1_loss_10: 0.0987 - dense_1_loss_11: 0.1126 - dense_1_loss_12: 0.1019 - dense_1_loss_13: 0.0948 - dense_1_loss_14: 0.0996 - dense_1_loss_15: 0.0984 - dense_1_loss_16: 0.0987 - dense_1_loss_17: 0.1031 - dense_1_loss_18: 0.0995 - dense_1_loss_19: 0.1077 - dense_1_loss_20: 0.1079 - dense_1_loss_21: 0.1046 - dense_1_loss_22: 0.1028 - dense_1_loss_23: 0.1023 - dense_1_loss_24: 0.0975 - dense_1_loss_25: 0.1167 - dense_1_loss_26: 0.1035 - dense_1_loss_27: 0.1052 - dense_1_loss_28: 0.1159 - dense_1_loss_29: 0.1220 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.8667 - dense_1_acc_4: 0.9500 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 59/100
60/60 [==============================] - 0s - loss: 9.8203 - dense_1_loss_1: 3.8939 - dense_1_loss_2: 1.9422 - dense_1_loss_3: 0.8429 - dense_1_loss_4: 0.3533 - dense_1_loss_5: 0.2323 - dense_1_loss_6: 0.1734 - dense_1_loss_7: 0.1676 - dense_1_loss_8: 0.1176 - dense_1_loss_9: 0.1153 - dense_1_loss_10: 0.0942 - dense_1_loss_11: 0.1060 - dense_1_loss_12: 0.0960 - dense_1_loss_13: 0.0904 - dense_1_loss_14: 0.0930 - dense_1_loss_15: 0.0923 - dense_1_loss_16: 0.0952 - dense_1_loss_17: 0.0974 - dense_1_loss_18: 0.0942 - dense_1_loss_19: 0.1022 - dense_1_loss_20: 0.1018 - dense_1_loss_21: 0.0990 - dense_1_loss_22: 0.0968 - dense_1_loss_23: 0.0966 - dense_1_loss_24: 0.0918 - dense_1_loss_25: 0.1124 - dense_1_loss_26: 0.0959 - dense_1_loss_27: 0.1002 - dense_1_loss_28: 0.1087 - dense_1_loss_29: 0.1179 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.8667 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 60/100
60/60 [==============================] - 0s - loss: 9.6102 - dense_1_loss_1: 3.8901 - dense_1_loss_2: 1.9130 - dense_1_loss_3: 0.8196 - dense_1_loss_4: 0.3378 - dense_1_loss_5: 0.2224 - dense_1_loss_6: 0.1642 - dense_1_loss_7: 0.1581 - dense_1_loss_8: 0.1135 - dense_1_loss_9: 0.1082 - dense_1_loss_10: 0.0912 - dense_1_loss_11: 0.0977 - dense_1_loss_12: 0.0921 - dense_1_loss_13: 0.0849 - dense_1_loss_14: 0.0853 - dense_1_loss_15: 0.0877 - dense_1_loss_16: 0.0951 - dense_1_loss_17: 0.0930 - dense_1_loss_18: 0.0896 - dense_1_loss_19: 0.0958 - dense_1_loss_20: 0.0967 - dense_1_loss_21: 0.0938 - dense_1_loss_22: 0.0912 - dense_1_loss_23: 0.0920 - dense_1_loss_24: 0.0874 - dense_1_loss_25: 0.1076 - dense_1_loss_26: 0.0912 - dense_1_loss_27: 0.0961 - dense_1_loss_28: 0.1043 - dense_1_loss_29: 0.1108 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.8833 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 61/100
60/60 [==============================] - 0s - loss: 9.4133 - dense_1_loss_1: 3.8863 - dense_1_loss_2: 1.8849 - dense_1_loss_3: 0.7978 - dense_1_loss_4: 0.3220 - dense_1_loss_5: 0.2128 - dense_1_loss_6: 0.1572 - dense_1_loss_7: 0.1521 - dense_1_loss_8: 0.1070 - dense_1_loss_9: 0.1030 - dense_1_loss_10: 0.0857 - dense_1_loss_11: 0.0937 - dense_1_loss_12: 0.0873 - dense_1_loss_13: 0.0807 - dense_1_loss_14: 0.0805 - dense_1_loss_15: 0.0843 - dense_1_loss_16: 0.0901 - dense_1_loss_17: 0.0880 - dense_1_loss_18: 0.0856 - dense_1_loss_19: 0.0903 - dense_1_loss_20: 0.0918 - dense_1_loss_21: 0.0891 - dense_1_loss_22: 0.0861 - dense_1_loss_23: 0.0880 - dense_1_loss_24: 0.0832 - dense_1_loss_25: 0.1017 - dense_1_loss_26: 0.0877 - dense_1_loss_27: 0.0909 - dense_1_loss_28: 0.1005 - dense_1_loss_29: 0.1050 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.8833 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 62/100
60/60 [==============================] - 0s - loss: 9.2328 - dense_1_loss_1: 3.8825 - dense_1_loss_2: 1.8573 - dense_1_loss_3: 0.7768 - dense_1_loss_4: 0.3090 - dense_1_loss_5: 0.2032 - dense_1_loss_6: 0.1514 - dense_1_loss_7: 0.1455 - dense_1_loss_8: 0.1013 - dense_1_loss_9: 0.0986 - dense_1_loss_10: 0.0806 - dense_1_loss_11: 0.0914 - dense_1_loss_12: 0.0821 - dense_1_loss_13: 0.0775 - dense_1_loss_14: 0.0791 - dense_1_loss_15: 0.0815 - dense_1_loss_16: 0.0832 - dense_1_loss_17: 0.0835 - dense_1_loss_18: 0.0818 - dense_1_loss_19: 0.0859 - dense_1_loss_20: 0.0878 - dense_1_loss_21: 0.0853 - dense_1_loss_22: 0.0816 - dense_1_loss_23: 0.0841 - dense_1_loss_24: 0.0788 - dense_1_loss_25: 0.0971 - dense_1_loss_26: 0.0838 - dense_1_loss_27: 0.0861 - dense_1_loss_28: 0.0956 - dense_1_loss_29: 0.1004 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5333 - dense_1_acc_3: 0.8833 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 63/100
60/60 [==============================] - 0s - loss: 9.0548 - dense_1_loss_1: 3.8792 - dense_1_loss_2: 1.8308 - dense_1_loss_3: 0.7560 - dense_1_loss_4: 0.2950 - dense_1_loss_5: 0.1932 - dense_1_loss_6: 0.1463 - dense_1_loss_7: 0.1384 - dense_1_loss_8: 0.0973 - dense_1_loss_9: 0.0945 - dense_1_loss_10: 0.0765 - dense_1_loss_11: 0.0868 - dense_1_loss_12: 0.0783 - dense_1_loss_13: 0.0737 - dense_1_loss_14: 0.0760 - dense_1_loss_15: 0.0766 - dense_1_loss_16: 0.0795 - dense_1_loss_17: 0.0796 - dense_1_loss_18: 0.0771 - dense_1_loss_19: 0.0826 - dense_1_loss_20: 0.0834 - dense_1_loss_21: 0.0810 - dense_1_loss_22: 0.0785 - dense_1_loss_23: 0.0788 - dense_1_loss_24: 0.0750 - dense_1_loss_25: 0.0926 - dense_1_loss_26: 0.0799 - dense_1_loss_27: 0.0815 - dense_1_loss_28: 0.0906 - dense_1_loss_29: 0.0961 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5333 - dense_1_acc_3: 0.8833 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 64/100
60/60 [==============================] - 0s - loss: 8.8914 - dense_1_loss_1: 3.8755 - dense_1_loss_2: 1.8055 - dense_1_loss_3: 0.7353 - dense_1_loss_4: 0.2827 - dense_1_loss_5: 0.1841 - dense_1_loss_6: 0.1409 - dense_1_loss_7: 0.1301 - dense_1_loss_8: 0.0936 - dense_1_loss_9: 0.0905 - dense_1_loss_10: 0.0738 - dense_1_loss_11: 0.0813 - dense_1_loss_12: 0.0759 - dense_1_loss_13: 0.0702 - dense_1_loss_14: 0.0716 - dense_1_loss_15: 0.0723 - dense_1_loss_16: 0.0781 - dense_1_loss_17: 0.0761 - dense_1_loss_18: 0.0736 - dense_1_loss_19: 0.0786 - dense_1_loss_20: 0.0793 - dense_1_loss_21: 0.0781 - dense_1_loss_22: 0.0746 - dense_1_loss_23: 0.0752 - dense_1_loss_24: 0.0720 - dense_1_loss_25: 0.0887 - dense_1_loss_26: 0.0761 - dense_1_loss_27: 0.0778 - dense_1_loss_28: 0.0867 - dense_1_loss_29: 0.0931 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5500 - dense_1_acc_3: 0.8833 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 65/100
60/60 [==============================] - 0s - loss: 8.7391 - dense_1_loss_1: 3.8723 - dense_1_loss_2: 1.7804 - dense_1_loss_3: 0.7166 - dense_1_loss_4: 0.2720 - dense_1_loss_5: 0.1767 - dense_1_loss_6: 0.1358 - dense_1_loss_7: 0.1244 - dense_1_loss_8: 0.0892 - dense_1_loss_9: 0.0861 - dense_1_loss_10: 0.0708 - dense_1_loss_11: 0.0780 - dense_1_loss_12: 0.0725 - dense_1_loss_13: 0.0674 - dense_1_loss_14: 0.0686 - dense_1_loss_15: 0.0695 - dense_1_loss_16: 0.0742 - dense_1_loss_17: 0.0727 - dense_1_loss_18: 0.0706 - dense_1_loss_19: 0.0747 - dense_1_loss_20: 0.0758 - dense_1_loss_21: 0.0747 - dense_1_loss_22: 0.0707 - dense_1_loss_23: 0.0721 - dense_1_loss_24: 0.0693 - dense_1_loss_25: 0.0842 - dense_1_loss_26: 0.0727 - dense_1_loss_27: 0.0748 - dense_1_loss_28: 0.0830 - dense_1_loss_29: 0.0892 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5500 - dense_1_acc_3: 0.8833 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 66/100
60/60 [==============================] - 0s - loss: 8.5942 - dense_1_loss_1: 3.8689 - dense_1_loss_2: 1.7559 - dense_1_loss_3: 0.6982 - dense_1_loss_4: 0.2609 - dense_1_loss_5: 0.1701 - dense_1_loss_6: 0.1306 - dense_1_loss_7: 0.1191 - dense_1_loss_8: 0.0851 - dense_1_loss_9: 0.0818 - dense_1_loss_10: 0.0680 - dense_1_loss_11: 0.0746 - dense_1_loss_12: 0.0693 - dense_1_loss_13: 0.0646 - dense_1_loss_14: 0.0660 - dense_1_loss_15: 0.0671 - dense_1_loss_16: 0.0708 - dense_1_loss_17: 0.0694 - dense_1_loss_18: 0.0676 - dense_1_loss_19: 0.0714 - dense_1_loss_20: 0.0726 - dense_1_loss_21: 0.0712 - dense_1_loss_22: 0.0675 - dense_1_loss_23: 0.0694 - dense_1_loss_24: 0.0669 - dense_1_loss_25: 0.0796 - dense_1_loss_26: 0.0696 - dense_1_loss_27: 0.0718 - dense_1_loss_28: 0.0809 - dense_1_loss_29: 0.0853 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5667 - dense_1_acc_3: 0.8833 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 67/100
60/60 [==============================] - 0s - loss: 8.4597 - dense_1_loss_1: 3.8653 - dense_1_loss_2: 1.7322 - dense_1_loss_3: 0.6813 - dense_1_loss_4: 0.2504 - dense_1_loss_5: 0.1646 - dense_1_loss_6: 0.1260 - dense_1_loss_7: 0.1150 - dense_1_loss_8: 0.0816 - dense_1_loss_9: 0.0788 - dense_1_loss_10: 0.0654 - dense_1_loss_11: 0.0715 - dense_1_loss_12: 0.0665 - dense_1_loss_13: 0.0620 - dense_1_loss_14: 0.0631 - dense_1_loss_15: 0.0643 - dense_1_loss_16: 0.0678 - dense_1_loss_17: 0.0667 - dense_1_loss_18: 0.0647 - dense_1_loss_19: 0.0686 - dense_1_loss_20: 0.0697 - dense_1_loss_21: 0.0677 - dense_1_loss_22: 0.0649 - dense_1_loss_23: 0.0665 - dense_1_loss_24: 0.0640 - dense_1_loss_25: 0.0765 - dense_1_loss_26: 0.0670 - dense_1_loss_27: 0.0691 - dense_1_loss_28: 0.0773 - dense_1_loss_29: 0.0814 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5833 - dense_1_acc_3: 0.8833 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 68/100
60/60 [==============================] - 0s - loss: 8.3313 - dense_1_loss_1: 3.8621 - dense_1_loss_2: 1.7093 - dense_1_loss_3: 0.6644 - dense_1_loss_4: 0.2410 - dense_1_loss_5: 0.1584 - dense_1_loss_6: 0.1217 - dense_1_loss_7: 0.1098 - dense_1_loss_8: 0.0788 - dense_1_loss_9: 0.0759 - dense_1_loss_10: 0.0629 - dense_1_loss_11: 0.0685 - dense_1_loss_12: 0.0640 - dense_1_loss_13: 0.0594 - dense_1_loss_14: 0.0603 - dense_1_loss_15: 0.0613 - dense_1_loss_16: 0.0656 - dense_1_loss_17: 0.0639 - dense_1_loss_18: 0.0619 - dense_1_loss_19: 0.0660 - dense_1_loss_20: 0.0670 - dense_1_loss_21: 0.0648 - dense_1_loss_22: 0.0628 - dense_1_loss_23: 0.0633 - dense_1_loss_24: 0.0610 - dense_1_loss_25: 0.0739 - dense_1_loss_26: 0.0641 - dense_1_loss_27: 0.0664 - dense_1_loss_28: 0.0742 - dense_1_loss_29: 0.0783 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.8833 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 69/100
60/60 [==============================] - 0s - loss: 8.2080 - dense_1_loss_1: 3.8588 - dense_1_loss_2: 1.6867 - dense_1_loss_3: 0.6497 - dense_1_loss_4: 0.2312 - dense_1_loss_5: 0.1523 - dense_1_loss_6: 0.1170 - dense_1_loss_7: 0.1045 - dense_1_loss_8: 0.0760 - dense_1_loss_9: 0.0730 - dense_1_loss_10: 0.0605 - dense_1_loss_11: 0.0660 - dense_1_loss_12: 0.0614 - dense_1_loss_13: 0.0570 - dense_1_loss_14: 0.0581 - dense_1_loss_15: 0.0590 - dense_1_loss_16: 0.0633 - dense_1_loss_17: 0.0611 - dense_1_loss_18: 0.0595 - dense_1_loss_19: 0.0633 - dense_1_loss_20: 0.0644 - dense_1_loss_21: 0.0625 - dense_1_loss_22: 0.0602 - dense_1_loss_23: 0.0608 - dense_1_loss_24: 0.0586 - dense_1_loss_25: 0.0714 - dense_1_loss_26: 0.0612 - dense_1_loss_27: 0.0638 - dense_1_loss_28: 0.0713 - dense_1_loss_29: 0.0755 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.8833 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 70/100
60/60 [==============================] - 0s - loss: 8.0932 - dense_1_loss_1: 3.8554 - dense_1_loss_2: 1.6644 - dense_1_loss_3: 0.6344 - dense_1_loss_4: 0.2231 - dense_1_loss_5: 0.1467 - dense_1_loss_6: 0.1132 - dense_1_loss_7: 0.1002 - dense_1_loss_8: 0.0731 - dense_1_loss_9: 0.0704 - dense_1_loss_10: 0.0579 - dense_1_loss_11: 0.0641 - dense_1_loss_12: 0.0590 - dense_1_loss_13: 0.0547 - dense_1_loss_14: 0.0564 - dense_1_loss_15: 0.0570 - dense_1_loss_16: 0.0605 - dense_1_loss_17: 0.0586 - dense_1_loss_18: 0.0574 - dense_1_loss_19: 0.0612 - dense_1_loss_20: 0.0617 - dense_1_loss_21: 0.0602 - dense_1_loss_22: 0.0581 - dense_1_loss_23: 0.0583 - dense_1_loss_24: 0.0565 - dense_1_loss_25: 0.0686 - dense_1_loss_26: 0.0590 - dense_1_loss_27: 0.0614 - dense_1_loss_28: 0.0689 - dense_1_loss_29: 0.0725 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.8833 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 71/100
60/60 [==============================] - 0s - loss: 7.9833 - dense_1_loss_1: 3.8521 - dense_1_loss_2: 1.6440 - dense_1_loss_3: 0.6199 - dense_1_loss_4: 0.2144 - dense_1_loss_5: 0.1410 - dense_1_loss_6: 0.1097 - dense_1_loss_7: 0.0962 - dense_1_loss_8: 0.0707 - dense_1_loss_9: 0.0678 - dense_1_loss_10: 0.0558 - dense_1_loss_11: 0.0620 - dense_1_loss_12: 0.0569 - dense_1_loss_13: 0.0526 - dense_1_loss_14: 0.0543 - dense_1_loss_15: 0.0549 - dense_1_loss_16: 0.0583 - dense_1_loss_17: 0.0564 - dense_1_loss_18: 0.0554 - dense_1_loss_19: 0.0587 - dense_1_loss_20: 0.0594 - dense_1_loss_21: 0.0580 - dense_1_loss_22: 0.0557 - dense_1_loss_23: 0.0561 - dense_1_loss_24: 0.0546 - dense_1_loss_25: 0.0658 - dense_1_loss_26: 0.0568 - dense_1_loss_27: 0.0590 - dense_1_loss_28: 0.0666 - dense_1_loss_29: 0.0701 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.8833 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 72/100
60/60 [==============================] - 0s - loss: 7.8793 - dense_1_loss_1: 3.8491 - dense_1_loss_2: 1.6233 - dense_1_loss_3: 0.6055 - dense_1_loss_4: 0.2075 - dense_1_loss_5: 0.1360 - dense_1_loss_6: 0.1063 - dense_1_loss_7: 0.0922 - dense_1_loss_8: 0.0684 - dense_1_loss_9: 0.0655 - dense_1_loss_10: 0.0538 - dense_1_loss_11: 0.0597 - dense_1_loss_12: 0.0548 - dense_1_loss_13: 0.0509 - dense_1_loss_14: 0.0519 - dense_1_loss_15: 0.0528 - dense_1_loss_16: 0.0567 - dense_1_loss_17: 0.0545 - dense_1_loss_18: 0.0533 - dense_1_loss_19: 0.0564 - dense_1_loss_20: 0.0573 - dense_1_loss_21: 0.0558 - dense_1_loss_22: 0.0536 - dense_1_loss_23: 0.0542 - dense_1_loss_24: 0.0528 - dense_1_loss_25: 0.0632 - dense_1_loss_26: 0.0548 - dense_1_loss_27: 0.0568 - dense_1_loss_28: 0.0645 - dense_1_loss_29: 0.0678 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.8833 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 73/100
60/60 [==============================] - 0s - loss: 7.7824 - dense_1_loss_1: 3.8456 - dense_1_loss_2: 1.6041 - dense_1_loss_3: 0.5923 - dense_1_loss_4: 0.2004 - dense_1_loss_5: 0.1317 - dense_1_loss_6: 0.1029 - dense_1_loss_7: 0.0889 - dense_1_loss_8: 0.0664 - dense_1_loss_9: 0.0632 - dense_1_loss_10: 0.0521 - dense_1_loss_11: 0.0574 - dense_1_loss_12: 0.0529 - dense_1_loss_13: 0.0493 - dense_1_loss_14: 0.0501 - dense_1_loss_15: 0.0510 - dense_1_loss_16: 0.0550 - dense_1_loss_17: 0.0527 - dense_1_loss_18: 0.0514 - dense_1_loss_19: 0.0545 - dense_1_loss_20: 0.0553 - dense_1_loss_21: 0.0539 - dense_1_loss_22: 0.0516 - dense_1_loss_23: 0.0523 - dense_1_loss_24: 0.0509 - dense_1_loss_25: 0.0612 - dense_1_loss_26: 0.0528 - dense_1_loss_27: 0.0548 - dense_1_loss_28: 0.0620 - dense_1_loss_29: 0.0656 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 74/100
60/60 [==============================] - 0s - loss: 7.6862 - dense_1_loss_1: 3.8425 - dense_1_loss_2: 1.5849 - dense_1_loss_3: 0.5793 - dense_1_loss_4: 0.1927 - dense_1_loss_5: 0.1273 - dense_1_loss_6: 0.0993 - dense_1_loss_7: 0.0849 - dense_1_loss_8: 0.0644 - dense_1_loss_9: 0.0610 - dense_1_loss_10: 0.0502 - dense_1_loss_11: 0.0555 - dense_1_loss_12: 0.0509 - dense_1_loss_13: 0.0478 - dense_1_loss_14: 0.0484 - dense_1_loss_15: 0.0494 - dense_1_loss_16: 0.0531 - dense_1_loss_17: 0.0509 - dense_1_loss_18: 0.0496 - dense_1_loss_19: 0.0527 - dense_1_loss_20: 0.0534 - dense_1_loss_21: 0.0521 - dense_1_loss_22: 0.0499 - dense_1_loss_23: 0.0506 - dense_1_loss_24: 0.0491 - dense_1_loss_25: 0.0593 - dense_1_loss_26: 0.0512 - dense_1_loss_27: 0.0528 - dense_1_loss_28: 0.0598 - dense_1_loss_29: 0.0633 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 75/100
60/60 [==============================] - 0s - loss: 7.5985 - dense_1_loss_1: 3.8396 - dense_1_loss_2: 1.5661 - dense_1_loss_3: 0.5669 - dense_1_loss_4: 0.1867 - dense_1_loss_5: 0.1231 - dense_1_loss_6: 0.0963 - dense_1_loss_7: 0.0820 - dense_1_loss_8: 0.0623 - dense_1_loss_9: 0.0592 - dense_1_loss_10: 0.0486 - dense_1_loss_11: 0.0536 - dense_1_loss_12: 0.0492 - dense_1_loss_13: 0.0461 - dense_1_loss_14: 0.0469 - dense_1_loss_15: 0.0479 - dense_1_loss_16: 0.0513 - dense_1_loss_17: 0.0492 - dense_1_loss_18: 0.0480 - dense_1_loss_19: 0.0510 - dense_1_loss_20: 0.0517 - dense_1_loss_21: 0.0506 - dense_1_loss_22: 0.0483 - dense_1_loss_23: 0.0489 - dense_1_loss_24: 0.0476 - dense_1_loss_25: 0.0574 - dense_1_loss_26: 0.0496 - dense_1_loss_27: 0.0511 - dense_1_loss_28: 0.0580 - dense_1_loss_29: 0.0615 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 76/100
60/60 [==============================] - 0s - loss: 7.5125 - dense_1_loss_1: 3.8364 - dense_1_loss_2: 1.5483 - dense_1_loss_3: 0.5546 - dense_1_loss_4: 0.1802 - dense_1_loss_5: 0.1192 - dense_1_loss_6: 0.0934 - dense_1_loss_7: 0.0791 - dense_1_loss_8: 0.0603 - dense_1_loss_9: 0.0576 - dense_1_loss_10: 0.0469 - dense_1_loss_11: 0.0519 - dense_1_loss_12: 0.0477 - dense_1_loss_13: 0.0446 - dense_1_loss_14: 0.0454 - dense_1_loss_15: 0.0463 - dense_1_loss_16: 0.0498 - dense_1_loss_17: 0.0475 - dense_1_loss_18: 0.0464 - dense_1_loss_19: 0.0493 - dense_1_loss_20: 0.0500 - dense_1_loss_21: 0.0490 - dense_1_loss_22: 0.0467 - dense_1_loss_23: 0.0472 - dense_1_loss_24: 0.0460 - dense_1_loss_25: 0.0555 - dense_1_loss_26: 0.0479 - dense_1_loss_27: 0.0494 - dense_1_loss_28: 0.0563 - dense_1_loss_29: 0.0593 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 77/100
60/60 [==============================] - 0s - loss: 7.4341 - dense_1_loss_1: 3.8334 - dense_1_loss_2: 1.5311 - dense_1_loss_3: 0.5435 - dense_1_loss_4: 0.1750 - dense_1_loss_5: 0.1158 - dense_1_loss_6: 0.0910 - dense_1_loss_7: 0.0767 - dense_1_loss_8: 0.0585 - dense_1_loss_9: 0.0560 - dense_1_loss_10: 0.0454 - dense_1_loss_11: 0.0504 - dense_1_loss_12: 0.0463 - dense_1_loss_13: 0.0432 - dense_1_loss_14: 0.0440 - dense_1_loss_15: 0.0450 - dense_1_loss_16: 0.0483 - dense_1_loss_17: 0.0461 - dense_1_loss_18: 0.0450 - dense_1_loss_19: 0.0478 - dense_1_loss_20: 0.0483 - dense_1_loss_21: 0.0475 - dense_1_loss_22: 0.0451 - dense_1_loss_23: 0.0458 - dense_1_loss_24: 0.0446 - dense_1_loss_25: 0.0535 - dense_1_loss_26: 0.0465 - dense_1_loss_27: 0.0480 - dense_1_loss_28: 0.0547 - dense_1_loss_29: 0.0576 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 78/100
60/60 [==============================] - 0s - loss: 7.3557 - dense_1_loss_1: 3.8301 - dense_1_loss_2: 1.5137 - dense_1_loss_3: 0.5326 - dense_1_loss_4: 0.1695 - dense_1_loss_5: 0.1125 - dense_1_loss_6: 0.0882 - dense_1_loss_7: 0.0739 - dense_1_loss_8: 0.0568 - dense_1_loss_9: 0.0543 - dense_1_loss_10: 0.0441 - dense_1_loss_11: 0.0487 - dense_1_loss_12: 0.0448 - dense_1_loss_13: 0.0419 - dense_1_loss_14: 0.0426 - dense_1_loss_15: 0.0435 - dense_1_loss_16: 0.0469 - dense_1_loss_17: 0.0447 - dense_1_loss_18: 0.0435 - dense_1_loss_19: 0.0463 - dense_1_loss_20: 0.0468 - dense_1_loss_21: 0.0460 - dense_1_loss_22: 0.0437 - dense_1_loss_23: 0.0444 - dense_1_loss_24: 0.0434 - dense_1_loss_25: 0.0519 - dense_1_loss_26: 0.0451 - dense_1_loss_27: 0.0468 - dense_1_loss_28: 0.0532 - dense_1_loss_29: 0.0558 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 79/100
60/60 [==============================] - 0s - loss: 7.2792 - dense_1_loss_1: 3.8274 - dense_1_loss_2: 1.4976 - dense_1_loss_3: 0.5203 - dense_1_loss_4: 0.1640 - dense_1_loss_5: 0.1092 - dense_1_loss_6: 0.0854 - dense_1_loss_7: 0.0712 - dense_1_loss_8: 0.0553 - dense_1_loss_9: 0.0525 - dense_1_loss_10: 0.0428 - dense_1_loss_11: 0.0473 - dense_1_loss_12: 0.0433 - dense_1_loss_13: 0.0407 - dense_1_loss_14: 0.0412 - dense_1_loss_15: 0.0423 - dense_1_loss_16: 0.0455 - dense_1_loss_17: 0.0433 - dense_1_loss_18: 0.0422 - dense_1_loss_19: 0.0449 - dense_1_loss_20: 0.0454 - dense_1_loss_21: 0.0445 - dense_1_loss_22: 0.0423 - dense_1_loss_23: 0.0432 - dense_1_loss_24: 0.0420 - dense_1_loss_25: 0.0507 - dense_1_loss_26: 0.0437 - dense_1_loss_27: 0.0455 - dense_1_loss_28: 0.0514 - dense_1_loss_29: 0.0542 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 80/100
60/60 [==============================] - 0s - loss: 7.2088 - dense_1_loss_1: 3.8242 - dense_1_loss_2: 1.4811 - dense_1_loss_3: 0.5108 - dense_1_loss_4: 0.1594 - dense_1_loss_5: 0.1063 - dense_1_loss_6: 0.0832 - dense_1_loss_7: 0.0692 - dense_1_loss_8: 0.0537 - dense_1_loss_9: 0.0511 - dense_1_loss_10: 0.0415 - dense_1_loss_11: 0.0459 - dense_1_loss_12: 0.0420 - dense_1_loss_13: 0.0396 - dense_1_loss_14: 0.0399 - dense_1_loss_15: 0.0412 - dense_1_loss_16: 0.0442 - dense_1_loss_17: 0.0419 - dense_1_loss_18: 0.0410 - dense_1_loss_19: 0.0436 - dense_1_loss_20: 0.0441 - dense_1_loss_21: 0.0431 - dense_1_loss_22: 0.0411 - dense_1_loss_23: 0.0420 - dense_1_loss_24: 0.0408 - dense_1_loss_25: 0.0494 - dense_1_loss_26: 0.0423 - dense_1_loss_27: 0.0441 - dense_1_loss_28: 0.0497 - dense_1_loss_29: 0.0524 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 81/100
60/60 [==============================] - 0s - loss: 7.1396 - dense_1_loss_1: 3.8213 - dense_1_loss_2: 1.4653 - dense_1_loss_3: 0.5001 - dense_1_loss_4: 0.1549 - dense_1_loss_5: 0.1030 - dense_1_loss_6: 0.0809 - dense_1_loss_7: 0.0669 - dense_1_loss_8: 0.0520 - dense_1_loss_9: 0.0497 - dense_1_loss_10: 0.0404 - dense_1_loss_11: 0.0447 - dense_1_loss_12: 0.0409 - dense_1_loss_13: 0.0384 - dense_1_loss_14: 0.0388 - dense_1_loss_15: 0.0400 - dense_1_loss_16: 0.0428 - dense_1_loss_17: 0.0407 - dense_1_loss_18: 0.0398 - dense_1_loss_19: 0.0422 - dense_1_loss_20: 0.0428 - dense_1_loss_21: 0.0420 - dense_1_loss_22: 0.0400 - dense_1_loss_23: 0.0406 - dense_1_loss_24: 0.0398 - dense_1_loss_25: 0.0478 - dense_1_loss_26: 0.0411 - dense_1_loss_27: 0.0429 - dense_1_loss_28: 0.0486 - dense_1_loss_29: 0.0511 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 82/100
60/60 [==============================] - 0s - loss: 7.0758 - dense_1_loss_1: 3.8183 - dense_1_loss_2: 1.4507 - dense_1_loss_3: 0.4906 - dense_1_loss_4: 0.1508 - dense_1_loss_5: 0.1003 - dense_1_loss_6: 0.0791 - dense_1_loss_7: 0.0651 - dense_1_loss_8: 0.0507 - dense_1_loss_9: 0.0485 - dense_1_loss_10: 0.0393 - dense_1_loss_11: 0.0435 - dense_1_loss_12: 0.0398 - dense_1_loss_13: 0.0374 - dense_1_loss_14: 0.0377 - dense_1_loss_15: 0.0388 - dense_1_loss_16: 0.0418 - dense_1_loss_17: 0.0396 - dense_1_loss_18: 0.0386 - dense_1_loss_19: 0.0410 - dense_1_loss_20: 0.0417 - dense_1_loss_21: 0.0407 - dense_1_loss_22: 0.0388 - dense_1_loss_23: 0.0395 - dense_1_loss_24: 0.0387 - dense_1_loss_25: 0.0460 - dense_1_loss_26: 0.0400 - dense_1_loss_27: 0.0417 - dense_1_loss_28: 0.0475 - dense_1_loss_29: 0.0497 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 83/100
60/60 [==============================] - 0s - loss: 7.0105 - dense_1_loss_1: 3.8154 - dense_1_loss_2: 1.4350 - dense_1_loss_3: 0.4811 - dense_1_loss_4: 0.1461 - dense_1_loss_5: 0.0975 - dense_1_loss_6: 0.0767 - dense_1_loss_7: 0.0629 - dense_1_loss_8: 0.0494 - dense_1_loss_9: 0.0473 - dense_1_loss_10: 0.0382 - dense_1_loss_11: 0.0422 - dense_1_loss_12: 0.0388 - dense_1_loss_13: 0.0363 - dense_1_loss_14: 0.0366 - dense_1_loss_15: 0.0375 - dense_1_loss_16: 0.0410 - dense_1_loss_17: 0.0385 - dense_1_loss_18: 0.0375 - dense_1_loss_19: 0.0398 - dense_1_loss_20: 0.0405 - dense_1_loss_21: 0.0396 - dense_1_loss_22: 0.0378 - dense_1_loss_23: 0.0382 - dense_1_loss_24: 0.0377 - dense_1_loss_25: 0.0446 - dense_1_loss_26: 0.0389 - dense_1_loss_27: 0.0406 - dense_1_loss_28: 0.0463 - dense_1_loss_29: 0.0484 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 84/100
60/60 [==============================] - 0s - loss: 6.9501 - dense_1_loss_1: 3.8125 - dense_1_loss_2: 1.4203 - dense_1_loss_3: 0.4719 - dense_1_loss_4: 0.1424 - dense_1_loss_5: 0.0951 - dense_1_loss_6: 0.0747 - dense_1_loss_7: 0.0612 - dense_1_loss_8: 0.0482 - dense_1_loss_9: 0.0460 - dense_1_loss_10: 0.0372 - dense_1_loss_11: 0.0412 - dense_1_loss_12: 0.0377 - dense_1_loss_13: 0.0353 - dense_1_loss_14: 0.0357 - dense_1_loss_15: 0.0366 - dense_1_loss_16: 0.0398 - dense_1_loss_17: 0.0374 - dense_1_loss_18: 0.0365 - dense_1_loss_19: 0.0388 - dense_1_loss_20: 0.0393 - dense_1_loss_21: 0.0385 - dense_1_loss_22: 0.0368 - dense_1_loss_23: 0.0372 - dense_1_loss_24: 0.0367 - dense_1_loss_25: 0.0435 - dense_1_loss_26: 0.0379 - dense_1_loss_27: 0.0395 - dense_1_loss_28: 0.0449 - dense_1_loss_29: 0.0473 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 85/100
60/60 [==============================] - 0s - loss: 6.8908 - dense_1_loss_1: 3.8096 - dense_1_loss_2: 1.4065 - dense_1_loss_3: 0.4622 - dense_1_loss_4: 0.1385 - dense_1_loss_5: 0.0926 - dense_1_loss_6: 0.0726 - dense_1_loss_7: 0.0596 - dense_1_loss_8: 0.0470 - dense_1_loss_9: 0.0448 - dense_1_loss_10: 0.0362 - dense_1_loss_11: 0.0401 - dense_1_loss_12: 0.0367 - dense_1_loss_13: 0.0344 - dense_1_loss_14: 0.0348 - dense_1_loss_15: 0.0358 - dense_1_loss_16: 0.0387 - dense_1_loss_17: 0.0364 - dense_1_loss_18: 0.0355 - dense_1_loss_19: 0.0379 - dense_1_loss_20: 0.0381 - dense_1_loss_21: 0.0375 - dense_1_loss_22: 0.0358 - dense_1_loss_23: 0.0363 - dense_1_loss_24: 0.0357 - dense_1_loss_25: 0.0427 - dense_1_loss_26: 0.0369 - dense_1_loss_27: 0.0384 - dense_1_loss_28: 0.0436 - dense_1_loss_29: 0.0460 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 86/100
60/60 [==============================] - 0s - loss: 6.8355 - dense_1_loss_1: 3.8069 - dense_1_loss_2: 1.3923 - dense_1_loss_3: 0.4541 - dense_1_loss_4: 0.1352 - dense_1_loss_5: 0.0904 - dense_1_loss_6: 0.0708 - dense_1_loss_7: 0.0582 - dense_1_loss_8: 0.0458 - dense_1_loss_9: 0.0436 - dense_1_loss_10: 0.0352 - dense_1_loss_11: 0.0392 - dense_1_loss_12: 0.0357 - dense_1_loss_13: 0.0335 - dense_1_loss_14: 0.0339 - dense_1_loss_15: 0.0349 - dense_1_loss_16: 0.0376 - dense_1_loss_17: 0.0355 - dense_1_loss_18: 0.0347 - dense_1_loss_19: 0.0370 - dense_1_loss_20: 0.0371 - dense_1_loss_21: 0.0366 - dense_1_loss_22: 0.0349 - dense_1_loss_23: 0.0354 - dense_1_loss_24: 0.0348 - dense_1_loss_25: 0.0417 - dense_1_loss_26: 0.0360 - dense_1_loss_27: 0.0373 - dense_1_loss_28: 0.0424 - dense_1_loss_29: 0.0448 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 87/100
60/60 [==============================] - 0s - loss: 6.7820 - dense_1_loss_1: 3.8038 - dense_1_loss_2: 1.3795 - dense_1_loss_3: 0.4460 - dense_1_loss_4: 0.1318 - dense_1_loss_5: 0.0884 - dense_1_loss_6: 0.0693 - dense_1_loss_7: 0.0568 - dense_1_loss_8: 0.0447 - dense_1_loss_9: 0.0426 - dense_1_loss_10: 0.0343 - dense_1_loss_11: 0.0381 - dense_1_loss_12: 0.0348 - dense_1_loss_13: 0.0326 - dense_1_loss_14: 0.0330 - dense_1_loss_15: 0.0340 - dense_1_loss_16: 0.0367 - dense_1_loss_17: 0.0346 - dense_1_loss_18: 0.0337 - dense_1_loss_19: 0.0359 - dense_1_loss_20: 0.0362 - dense_1_loss_21: 0.0356 - dense_1_loss_22: 0.0339 - dense_1_loss_23: 0.0346 - dense_1_loss_24: 0.0339 - dense_1_loss_25: 0.0404 - dense_1_loss_26: 0.0351 - dense_1_loss_27: 0.0364 - dense_1_loss_28: 0.0414 - dense_1_loss_29: 0.0438 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 88/100
60/60 [==============================] - 0s - loss: 6.7287 - dense_1_loss_1: 3.8010 - dense_1_loss_2: 1.3661 - dense_1_loss_3: 0.4374 - dense_1_loss_4: 0.1286 - dense_1_loss_5: 0.0861 - dense_1_loss_6: 0.0677 - dense_1_loss_7: 0.0553 - dense_1_loss_8: 0.0436 - dense_1_loss_9: 0.0417 - dense_1_loss_10: 0.0335 - dense_1_loss_11: 0.0371 - dense_1_loss_12: 0.0341 - dense_1_loss_13: 0.0317 - dense_1_loss_14: 0.0321 - dense_1_loss_15: 0.0331 - dense_1_loss_16: 0.0360 - dense_1_loss_17: 0.0338 - dense_1_loss_18: 0.0329 - dense_1_loss_19: 0.0350 - dense_1_loss_20: 0.0353 - dense_1_loss_21: 0.0347 - dense_1_loss_22: 0.0330 - dense_1_loss_23: 0.0336 - dense_1_loss_24: 0.0331 - dense_1_loss_25: 0.0393 - dense_1_loss_26: 0.0341 - dense_1_loss_27: 0.0356 - dense_1_loss_28: 0.0406 - dense_1_loss_29: 0.0427 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 89/100
60/60 [==============================] - 0s - loss: 6.6773 - dense_1_loss_1: 3.7981 - dense_1_loss_2: 1.3530 - dense_1_loss_3: 0.4293 - dense_1_loss_4: 0.1254 - dense_1_loss_5: 0.0840 - dense_1_loss_6: 0.0658 - dense_1_loss_7: 0.0538 - dense_1_loss_8: 0.0427 - dense_1_loss_9: 0.0406 - dense_1_loss_10: 0.0327 - dense_1_loss_11: 0.0361 - dense_1_loss_12: 0.0332 - dense_1_loss_13: 0.0309 - dense_1_loss_14: 0.0313 - dense_1_loss_15: 0.0324 - dense_1_loss_16: 0.0353 - dense_1_loss_17: 0.0330 - dense_1_loss_18: 0.0321 - dense_1_loss_19: 0.0341 - dense_1_loss_20: 0.0345 - dense_1_loss_21: 0.0339 - dense_1_loss_22: 0.0322 - dense_1_loss_23: 0.0328 - dense_1_loss_24: 0.0324 - dense_1_loss_25: 0.0384 - dense_1_loss_26: 0.0333 - dense_1_loss_27: 0.0348 - dense_1_loss_28: 0.0397 - dense_1_loss_29: 0.0417 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 90/100
60/60 [==============================] - 0s - loss: 6.6297 - dense_1_loss_1: 3.7953 - dense_1_loss_2: 1.3410 - dense_1_loss_3: 0.4219 - dense_1_loss_4: 0.1224 - dense_1_loss_5: 0.0822 - dense_1_loss_6: 0.0643 - dense_1_loss_7: 0.0526 - dense_1_loss_8: 0.0418 - dense_1_loss_9: 0.0397 - dense_1_loss_10: 0.0319 - dense_1_loss_11: 0.0354 - dense_1_loss_12: 0.0324 - dense_1_loss_13: 0.0302 - dense_1_loss_14: 0.0305 - dense_1_loss_15: 0.0317 - dense_1_loss_16: 0.0344 - dense_1_loss_17: 0.0321 - dense_1_loss_18: 0.0313 - dense_1_loss_19: 0.0334 - dense_1_loss_20: 0.0336 - dense_1_loss_21: 0.0331 - dense_1_loss_22: 0.0315 - dense_1_loss_23: 0.0320 - dense_1_loss_24: 0.0315 - dense_1_loss_25: 0.0376 - dense_1_loss_26: 0.0325 - dense_1_loss_27: 0.0340 - dense_1_loss_28: 0.0387 - dense_1_loss_29: 0.0407 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 91/100
60/60 [==============================] - 0s - loss: 6.5828 - dense_1_loss_1: 3.7927 - dense_1_loss_2: 1.3288 - dense_1_loss_3: 0.4146 - dense_1_loss_4: 0.1198 - dense_1_loss_5: 0.0803 - dense_1_loss_6: 0.0626 - dense_1_loss_7: 0.0514 - dense_1_loss_8: 0.0409 - dense_1_loss_9: 0.0387 - dense_1_loss_10: 0.0311 - dense_1_loss_11: 0.0347 - dense_1_loss_12: 0.0316 - dense_1_loss_13: 0.0295 - dense_1_loss_14: 0.0299 - dense_1_loss_15: 0.0311 - dense_1_loss_16: 0.0334 - dense_1_loss_17: 0.0313 - dense_1_loss_18: 0.0306 - dense_1_loss_19: 0.0326 - dense_1_loss_20: 0.0328 - dense_1_loss_21: 0.0323 - dense_1_loss_22: 0.0307 - dense_1_loss_23: 0.0313 - dense_1_loss_24: 0.0308 - dense_1_loss_25: 0.0368 - dense_1_loss_26: 0.0317 - dense_1_loss_27: 0.0332 - dense_1_loss_28: 0.0377 - dense_1_loss_29: 0.0397 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 92/100
60/60 [==============================] - 0s - loss: 6.5360 - dense_1_loss_1: 3.7898 - dense_1_loss_2: 1.3168 - dense_1_loss_3: 0.4072 - dense_1_loss_4: 0.1168 - dense_1_loss_5: 0.0785 - dense_1_loss_6: 0.0611 - dense_1_loss_7: 0.0502 - dense_1_loss_8: 0.0400 - dense_1_loss_9: 0.0379 - dense_1_loss_10: 0.0304 - dense_1_loss_11: 0.0339 - dense_1_loss_12: 0.0309 - dense_1_loss_13: 0.0289 - dense_1_loss_14: 0.0292 - dense_1_loss_15: 0.0304 - dense_1_loss_16: 0.0326 - dense_1_loss_17: 0.0306 - dense_1_loss_18: 0.0298 - dense_1_loss_19: 0.0319 - dense_1_loss_20: 0.0320 - dense_1_loss_21: 0.0315 - dense_1_loss_22: 0.0301 - dense_1_loss_23: 0.0306 - dense_1_loss_24: 0.0301 - dense_1_loss_25: 0.0358 - dense_1_loss_26: 0.0310 - dense_1_loss_27: 0.0324 - dense_1_loss_28: 0.0369 - dense_1_loss_29: 0.0387 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 93/100
60/60 [==============================] - 0s - loss: 6.4926 - dense_1_loss_1: 3.7870 - dense_1_loss_2: 1.3053 - dense_1_loss_3: 0.4006 - dense_1_loss_4: 0.1143 - dense_1_loss_5: 0.0767 - dense_1_loss_6: 0.0598 - dense_1_loss_7: 0.0491 - dense_1_loss_8: 0.0391 - dense_1_loss_9: 0.0371 - dense_1_loss_10: 0.0297 - dense_1_loss_11: 0.0331 - dense_1_loss_12: 0.0302 - dense_1_loss_13: 0.0282 - dense_1_loss_14: 0.0286 - dense_1_loss_15: 0.0296 - dense_1_loss_16: 0.0320 - dense_1_loss_17: 0.0299 - dense_1_loss_18: 0.0292 - dense_1_loss_19: 0.0311 - dense_1_loss_20: 0.0313 - dense_1_loss_21: 0.0307 - dense_1_loss_22: 0.0295 - dense_1_loss_23: 0.0299 - dense_1_loss_24: 0.0295 - dense_1_loss_25: 0.0349 - dense_1_loss_26: 0.0304 - dense_1_loss_27: 0.0317 - dense_1_loss_28: 0.0362 - dense_1_loss_29: 0.0379 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 94/100
60/60 [==============================] - 0s - loss: 6.4500 - dense_1_loss_1: 3.7845 - dense_1_loss_2: 1.2939 - dense_1_loss_3: 0.3931 - dense_1_loss_4: 0.1121 - dense_1_loss_5: 0.0750 - dense_1_loss_6: 0.0586 - dense_1_loss_7: 0.0481 - dense_1_loss_8: 0.0383 - dense_1_loss_9: 0.0363 - dense_1_loss_10: 0.0291 - dense_1_loss_11: 0.0323 - dense_1_loss_12: 0.0296 - dense_1_loss_13: 0.0275 - dense_1_loss_14: 0.0280 - dense_1_loss_15: 0.0289 - dense_1_loss_16: 0.0315 - dense_1_loss_17: 0.0293 - dense_1_loss_18: 0.0285 - dense_1_loss_19: 0.0305 - dense_1_loss_20: 0.0306 - dense_1_loss_21: 0.0300 - dense_1_loss_22: 0.0288 - dense_1_loss_23: 0.0292 - dense_1_loss_24: 0.0289 - dense_1_loss_25: 0.0341 - dense_1_loss_26: 0.0297 - dense_1_loss_27: 0.0311 - dense_1_loss_28: 0.0354 - dense_1_loss_29: 0.0371 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 95/100
60/60 [==============================] - 0s - loss: 6.4081 - dense_1_loss_1: 3.7817 - dense_1_loss_2: 1.2828 - dense_1_loss_3: 0.3861 - dense_1_loss_4: 0.1097 - dense_1_loss_5: 0.0733 - dense_1_loss_6: 0.0572 - dense_1_loss_7: 0.0471 - dense_1_loss_8: 0.0375 - dense_1_loss_9: 0.0355 - dense_1_loss_10: 0.0285 - dense_1_loss_11: 0.0317 - dense_1_loss_12: 0.0289 - dense_1_loss_13: 0.0269 - dense_1_loss_14: 0.0273 - dense_1_loss_15: 0.0283 - dense_1_loss_16: 0.0308 - dense_1_loss_17: 0.0286 - dense_1_loss_18: 0.0279 - dense_1_loss_19: 0.0297 - dense_1_loss_20: 0.0300 - dense_1_loss_21: 0.0294 - dense_1_loss_22: 0.0282 - dense_1_loss_23: 0.0286 - dense_1_loss_24: 0.0283 - dense_1_loss_25: 0.0334 - dense_1_loss_26: 0.0291 - dense_1_loss_27: 0.0304 - dense_1_loss_28: 0.0348 - dense_1_loss_29: 0.0364 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 96/100
60/60 [==============================] - 0s - loss: 6.3681 - dense_1_loss_1: 3.7790 - dense_1_loss_2: 1.2717 - dense_1_loss_3: 0.3798 - dense_1_loss_4: 0.1075 - dense_1_loss_5: 0.0719 - dense_1_loss_6: 0.0562 - dense_1_loss_7: 0.0461 - dense_1_loss_8: 0.0367 - dense_1_loss_9: 0.0348 - dense_1_loss_10: 0.0279 - dense_1_loss_11: 0.0311 - dense_1_loss_12: 0.0283 - dense_1_loss_13: 0.0264 - dense_1_loss_14: 0.0268 - dense_1_loss_15: 0.0278 - dense_1_loss_16: 0.0301 - dense_1_loss_17: 0.0280 - dense_1_loss_18: 0.0273 - dense_1_loss_19: 0.0291 - dense_1_loss_20: 0.0293 - dense_1_loss_21: 0.0287 - dense_1_loss_22: 0.0275 - dense_1_loss_23: 0.0279 - dense_1_loss_24: 0.0277 - dense_1_loss_25: 0.0327 - dense_1_loss_26: 0.0284 - dense_1_loss_27: 0.0298 - dense_1_loss_28: 0.0339 - dense_1_loss_29: 0.0356 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 97/100
60/60 [==============================] - 0s - loss: 6.3282 - dense_1_loss_1: 3.7764 - dense_1_loss_2: 1.2606 - dense_1_loss_3: 0.3734 - dense_1_loss_4: 0.1054 - dense_1_loss_5: 0.0702 - dense_1_loss_6: 0.0549 - dense_1_loss_7: 0.0451 - dense_1_loss_8: 0.0359 - dense_1_loss_9: 0.0341 - dense_1_loss_10: 0.0273 - dense_1_loss_11: 0.0305 - dense_1_loss_12: 0.0276 - dense_1_loss_13: 0.0258 - dense_1_loss_14: 0.0263 - dense_1_loss_15: 0.0273 - dense_1_loss_16: 0.0293 - dense_1_loss_17: 0.0274 - dense_1_loss_18: 0.0268 - dense_1_loss_19: 0.0285 - dense_1_loss_20: 0.0287 - dense_1_loss_21: 0.0282 - dense_1_loss_22: 0.0270 - dense_1_loss_23: 0.0274 - dense_1_loss_24: 0.0271 - dense_1_loss_25: 0.0320 - dense_1_loss_26: 0.0278 - dense_1_loss_27: 0.0292 - dense_1_loss_28: 0.0332 - dense_1_loss_29: 0.0348 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 98/100
60/60 [==============================] - 0s - loss: 6.2921 - dense_1_loss_1: 3.7737 - dense_1_loss_2: 1.2509 - dense_1_loss_3: 0.3680 - dense_1_loss_4: 0.1033 - dense_1_loss_5: 0.0688 - dense_1_loss_6: 0.0540 - dense_1_loss_7: 0.0442 - dense_1_loss_8: 0.0352 - dense_1_loss_9: 0.0335 - dense_1_loss_10: 0.0267 - dense_1_loss_11: 0.0299 - dense_1_loss_12: 0.0271 - dense_1_loss_13: 0.0253 - dense_1_loss_14: 0.0257 - dense_1_loss_15: 0.0267 - dense_1_loss_16: 0.0288 - dense_1_loss_17: 0.0268 - dense_1_loss_18: 0.0263 - dense_1_loss_19: 0.0279 - dense_1_loss_20: 0.0281 - dense_1_loss_21: 0.0276 - dense_1_loss_22: 0.0264 - dense_1_loss_23: 0.0268 - dense_1_loss_24: 0.0265 - dense_1_loss_25: 0.0313 - dense_1_loss_26: 0.0272 - dense_1_loss_27: 0.0286 - dense_1_loss_28: 0.0326 - dense_1_loss_29: 0.0341 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 99/100
60/60 [==============================] - 0s - loss: 6.2552 - dense_1_loss_1: 3.7710 - dense_1_loss_2: 1.2403 - dense_1_loss_3: 0.3620 - dense_1_loss_4: 0.1015 - dense_1_loss_5: 0.0674 - dense_1_loss_6: 0.0530 - dense_1_loss_7: 0.0433 - dense_1_loss_8: 0.0346 - dense_1_loss_9: 0.0329 - dense_1_loss_10: 0.0262 - dense_1_loss_11: 0.0292 - dense_1_loss_12: 0.0266 - dense_1_loss_13: 0.0247 - dense_1_loss_14: 0.0251 - dense_1_loss_15: 0.0261 - dense_1_loss_16: 0.0285 - dense_1_loss_17: 0.0263 - dense_1_loss_18: 0.0257 - dense_1_loss_19: 0.0273 - dense_1_loss_20: 0.0275 - dense_1_loss_21: 0.0270 - dense_1_loss_22: 0.0258 - dense_1_loss_23: 0.0263 - dense_1_loss_24: 0.0260 - dense_1_loss_25: 0.0307 - dense_1_loss_26: 0.0267 - dense_1_loss_27: 0.0280 - dense_1_loss_28: 0.0320 - dense_1_loss_29: 0.0334 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 100/100
60/60 [==============================] - 0s - loss: 6.2195 - dense_1_loss_1: 3.7686 - dense_1_loss_2: 1.2304 - dense_1_loss_3: 0.3562 - dense_1_loss_4: 0.0996 - dense_1_loss_5: 0.0661 - dense_1_loss_6: 0.0518 - dense_1_loss_7: 0.0425 - dense_1_loss_8: 0.0339 - dense_1_loss_9: 0.0322 - dense_1_loss_10: 0.0257 - dense_1_loss_11: 0.0285 - dense_1_loss_12: 0.0261 - dense_1_loss_13: 0.0242 - dense_1_loss_14: 0.0246 - dense_1_loss_15: 0.0256 - dense_1_loss_16: 0.0281 - dense_1_loss_17: 0.0257 - dense_1_loss_18: 0.0252 - dense_1_loss_19: 0.0267 - dense_1_loss_20: 0.0269 - dense_1_loss_21: 0.0265 - dense_1_loss_22: 0.0253 - dense_1_loss_23: 0.0257 - dense_1_loss_24: 0.0255 - dense_1_loss_25: 0.0301 - dense_1_loss_26: 0.0261 - dense_1_loss_27: 0.0275 - dense_1_loss_28: 0.0314 - dense_1_loss_29: 0.0328 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6500 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     





<keras.callbacks.History at 0x7fcff481d908>

You should see the model loss going down. Now that you have trained a model, lets go on the the final section to implement an inference algorithm, and generate some music!

3 - Generating music

You now have a trained model which has learned the patterns of the jazz soloist. Lets now use this model to synthesize new music.

3.1 - Predicting & Sampling

At each step of sampling, you will take as input the activation a and cell state c from the previous state of the LSTM, forward propagate by one step, and get a new output activation as well as cell state. The new activation a can then be used to generate the output, using densor as before.

To start off the model, we will initialize x0 as well as the LSTM activation and and cell value a0 and c0 to be zeros.

Exercise: Implement the function below to sample a sequence of musical values. Here are some of the key steps you’ll need to implement inside the for-loop that generates the $T_y$ output characters:

Step 2.A: Use LSTM_Cell, which inputs the previous step’s c and a to generate the current step’s c and a.

Step 2.B: Use densor (defined previously) to compute a softmax on a to get the output for the current step.

Step 2.C: Save the output you have just generated by appending it to outputs.

Step 2.D: Sample x to the be “out”‘s one-hot version (the prediction) so that you can pass it to the next LSTM’s step. We have already provided this line of code, which uses a Lambda function.

1	x = Lambda(one_hot)(out)

[Minor technical note: Rather than sampling a value at random according to the probabilities in out, this line of code actually chooses the single most likely note at each step using an argmax.]

# GRADED FUNCTION: music_inference_model

def music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 100):
    """
    Uses the trained "LSTM_cell" and "densor" from model() to generate a sequence of values.

    Arguments:
    LSTM_cell -- the trained "LSTM_cell" from model(), Keras layer object
    densor -- the trained "densor" from model(), Keras layer object
    n_values -- integer, umber of unique values
    n_a -- number of units in the LSTM_cell
    Ty -- integer, number of time steps to generate

    Returns:
    inference_model -- Keras model instance
    """

    # Define the input of your model with a shape 
    x0 = Input(shape=(1, n_values))

    # Define s0, initial hidden state for the decoder LSTM
    a0 = Input(shape=(n_a,), name='a0')
    c0 = Input(shape=(n_a,), name='c0')
    a = a0
    c = c0
    x = x0

    ### START CODE HERE ###
    # Step 1: Create an empty list of "outputs" to later store your predicted values (≈1 line)
    outputs = []

    # Step 2: Loop over Ty and generate a value at every time step
    for t in range(Ty):

        # Step 2.A: Perform one step of LSTM_cell (≈1 line)
        a, _, c = LSTM_cell(x, initial_state=[a, c]);

        # Step 2.B: Apply Dense layer to the hidden state output of the LSTM_cell (≈1 line)
        out = densor(a);

        # Step 2.C: Append the prediction "out" to "outputs". out.shape = (None, 78) (≈1 line)
        outputs.append(out);

        # Step 2.D: Select the next value according to "out", and set "x" to be the one-hot representation of the
        #           selected value, which will be passed as the input to LSTM_cell on the next step. We have provided 
        #           the line of code you need to do this. 
        x = Lambda(one_hot)(out);

    # Step 3: Create model instance with the correct "inputs" and "outputs" (≈1 line)
    inference_model = Model(inputs=[x0, a0, c0], outputs=outputs);

    ### END CODE HERE ###

    return inference_model

Run the cell below to define your inference model. This model is hard coded to generate 50 values.

1	inference_model = music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 50)

Finally, this creates the zero-valued vectors you will use to initialize x and the LSTM state variables a and c.

1
2
3

x_initializer = np.zeros((1, 1, 78))
a_initializer = np.zeros((1, n_a))
c_initializer = np.zeros((1, n_a))

Exercise: Implement predict_and_sample(). This function takes many arguments including the inputs [x_initializer, a_initializer, c_initializer]. In order to predict the output corresponding to this input, you will need to carry-out 3 steps:

Use your inference model to predict an output given your set of inputs. The output pred should be a list of length $T_y$ where each element is a numpy-array of shape (1, n_values).
Convert pred into a numpy array of $T_y$ indices. Each index corresponds is computed by taking the argmax of an element of the pred list. Hint.
Convert the indices into their one-hot vector representations. Hint.

# GRADED FUNCTION: predict_and_sample

def predict_and_sample(inference_model, x_initializer = x_initializer, a_initializer = a_initializer, 
                       c_initializer = c_initializer):
    """
    Predicts the next value of values using the inference model.
    
    Arguments:
    inference_model -- Keras model instance for inference time
    x_initializer -- numpy array of shape (1, 1, 78), one-hot vector initializing the values generation
    a_initializer -- numpy array of shape (1, n_a), initializing the hidden state of the LSTM_cell
    c_initializer -- numpy array of shape (1, n_a), initializing the cell state of the LSTM_cel
    
    Returns:
    results -- numpy-array of shape (Ty, 78), matrix of one-hot vectors representing the values generated
    indices -- numpy-array of shape (Ty, 1), matrix of indices representing the values generated
    """
    
    ### START CODE HERE ###
    # Step 1: Use your inference model to predict an output sequence given x_initializer, a_initializer and c_initializer.
    pred = inference_model.predict([x_initializer, a_initializer, c_initializer]);
    # Step 2: Convert "pred" into an np.array() of indices with the maximum probabilities
    indices = np.argmax(np.array(pred), axis = -1);
    # Step 3: Convert indices to one-hot vectors, the shape of the results should be (1, )
    results = to_categorical(indices, num_classes = x_initializer.shape[-1]);
    ### END CODE HERE ###
    
    return results, indices

results, indices = predict_and_sample(inference_model, x_initializer, a_initializer, c_initializer)
print("np.argmax(results[12]) =", np.argmax(results[12]))
print("np.argmax(results[17]) =", np.argmax(results[17]))
print("list(indices[12:18]) =", list(indices[12:18]))

np.argmax(results[12]) = 21
np.argmax(results[17]) = 7
list(indices[12:18]) = [array([21]), array([10]), array([57]), array([43]), array([12]), array([7])]

Expected Output: Your results may differ because Keras’ results are not completely predictable. However, if you have trained your LSTM_cell with model.fit() for exactly 100 epochs as described above, you should very likely observe a sequence of indices that are not all identical. Moreover, you should observe that: np.argmax(results[12]) is the first element of list(indices[12:18]) and np.argmax(results[17]) is the last element of list(indices[12:18]).

np.argmax(results[12]) =	1
np.argmax(results[12]) =	42
list(indices[12:18]) =	[array([1]), array([42]), array([54]), array([17]), array([1]), array([42])]

3.3 - Generate music

Finally, you are ready to generate music. Your RNN generates a sequence of values. The following code generates music by first calling your predict_and_sample() function. These values are then post-processed into musical chords (meaning that multiple values or notes can be played at the same time).

Most computational music algorithms use some post-processing because it is difficult to generate music that sounds good without such post-processing. The post-processing does things such as clean up the generated audio by making sure the same sound is not repeated too many times, that two successive notes are not too far from each other in pitch, and so on. One could argue that a lot of these post-processing steps are hacks; also, a lot the music generation literature has also focused on hand-crafting post-processors, and a lot of the output quality depends on the quality of the post-processing and not just the quality of the RNN. But this post-processing does make a huge difference, so lets use it in our implementation as well.

Lets make some music!

Run the following cell to generate music and record it into your out_stream. This can take a couple of minutes.

1	out_stream = generate_music(inference_model)

Predicting new values for different set of chords.
Generated 51 sounds using the predicted values for the set of chords ("1") and after pruning
Generated 50 sounds using the predicted values for the set of chords ("2") and after pruning
Generated 50 sounds using the predicted values for the set of chords ("3") and after pruning
Generated 51 sounds using the predicted values for the set of chords ("4") and after pruning
Generated 51 sounds using the predicted values for the set of chords ("5") and after pruning
Your generated music is saved in output/my_music.midi

To listen to your music, click File->Open… Then go to “output/“ and download “my_music.midi”. Either play it on your computer with an application that can read midi files if you have one, or use one of the free online “MIDI to mp3” conversion tools to convert this to mp3.

As reference, here also is a 30sec audio clip we generated using this algorithm.

1	IPython.display.Audio('./data/30s_trained_model.mp3')

            <audio controls="controls" >

y
Your browser does not support the audio element.

Congratulations!

You have come to the end of the notebook.

Here's what you should remember: - A sequence model can be used to generate musical values, which are then post-processed into midi music. - Fairly similar models can be used to generate dinosaur names or to generate music, with the major difference being the input fed to the model. - In Keras, sequence generation involves defining layers with shared weights, which are then repeated for the different time steps $1, \ldots, T_x$.

Congratulations on completing this assignment and generating a jazz solo!

References

The ideas presented in this notebook came primarily from three computational music papers cited below. The implementation here also took significant inspiration and used many components from Ji-Sung Kim’s github repository.

Ji-Sung Kim, 2016, deepjazz
Jon Gillick, Kevin Tang and Robert Keller, 2009. Learning Jazz Grammars
Robert Keller and David Morrison, 2007, A Grammatical Approach to Automatic Improvisation
François Pachet, 1999, Surprising Harmonies

We’re also grateful to François Germain for valuable feedback.

np.argmax(results[12]) =	1
np.argmax(results[12]) =	42
list(indices[12:18]) =	[array([1]), array([42]), array([54]), array([17]), array([1]), array([42])]