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.



In [1]:

    
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:



In [2]:

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









    Out[2]:

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.



In [3]:

    
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.



In [4]:

    
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().



In [5]:

    
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:
```
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"



In [6]:

    
# 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"
        outputs.append(out)
        
    # Step 3: Create model instance
    model = Model(inputs=[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.



In [7]:

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

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



In [8]:

    
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.



In [9]:

    
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.



In [10]:

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









    



Epoch 1/100
60/60 [==============================] - 7s - loss: 125.8683 - dense_1_loss_1: 4.3556 - dense_1_loss_2: 4.3536 - dense_1_loss_3: 4.3434 - dense_1_loss_4: 4.3413 - dense_1_loss_5: 4.3426 - dense_1_loss_6: 4.3399 - dense_1_loss_7: 4.3407 - dense_1_loss_8: 4.3423 - dense_1_loss_9: 4.3435 - dense_1_loss_10: 4.3406 - dense_1_loss_11: 4.3372 - dense_1_loss_12: 4.3454 - dense_1_loss_13: 4.3392 - dense_1_loss_14: 4.3353 - dense_1_loss_15: 4.3361 - dense_1_loss_16: 4.3352 - dense_1_loss_17: 4.3405 - dense_1_loss_18: 4.3383 - dense_1_loss_19: 4.3312 - dense_1_loss_20: 4.3451 - dense_1_loss_21: 4.3465 - dense_1_loss_22: 4.3431 - dense_1_loss_23: 4.3294 - dense_1_loss_24: 4.3319 - dense_1_loss_25: 4.3353 - dense_1_loss_26: 4.3381 - dense_1_loss_27: 4.3380 - dense_1_loss_28: 4.3404 - dense_1_loss_29: 4.3384 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0000e+00 - dense_1_acc_2: 0.0333 - dense_1_acc_3: 0.0167 - dense_1_acc_4: 0.0500 - dense_1_acc_5: 0.0167 - dense_1_acc_6: 0.0167 - dense_1_acc_7: 0.0333 - dense_1_acc_8: 0.0500 - dense_1_acc_9: 0.0333 - dense_1_acc_10: 0.0000e+00 - dense_1_acc_11: 0.0167 - dense_1_acc_12: 0.0167 - dense_1_acc_13: 0.0167 - dense_1_acc_14: 0.0500 - dense_1_acc_15: 0.0667 - dense_1_acc_16: 0.0333 - dense_1_acc_17: 0.0500 - dense_1_acc_18: 0.0167 - dense_1_acc_19: 0.0667 - dense_1_acc_20: 0.0333 - dense_1_acc_21: 0.0000e+00 - dense_1_acc_22: 0.0333 - dense_1_acc_23: 0.0333 - dense_1_acc_24: 0.0500 - dense_1_acc_25: 0.0167 - dense_1_acc_26: 0.0167 - dense_1_acc_27: 0.0167 - dense_1_acc_28: 0.0333 - dense_1_acc_29: 0.0500 - dense_1_acc_30: 0.0000e+00                                                                                         
Epoch 2/100
60/60 [==============================] - 0s - loss: 122.5092 - dense_1_loss_1: 4.3345 - dense_1_loss_2: 4.3110 - dense_1_loss_3: 4.2801 - dense_1_loss_4: 4.2753 - dense_1_loss_5: 4.2571 - dense_1_loss_6: 4.2572 - dense_1_loss_7: 4.2506 - dense_1_loss_8: 4.2280 - dense_1_loss_9: 4.2445 - dense_1_loss_10: 4.2164 - dense_1_loss_11: 4.2136 - dense_1_loss_12: 4.2201 - dense_1_loss_13: 4.1955 - dense_1_loss_14: 4.1979 - dense_1_loss_15: 4.1978 - dense_1_loss_16: 4.1993 - dense_1_loss_17: 4.1999 - dense_1_loss_18: 4.2244 - dense_1_loss_19: 4.1808 - dense_1_loss_20: 4.2230 - dense_1_loss_21: 4.2254 - dense_1_loss_22: 4.2003 - dense_1_loss_23: 4.1750 - dense_1_loss_24: 4.2133 - dense_1_loss_25: 4.2197 - dense_1_loss_26: 4.1668 - dense_1_loss_27: 4.2022 - dense_1_loss_28: 4.1920 - dense_1_loss_29: 4.2077 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0333 - dense_1_acc_2: 0.0667 - dense_1_acc_3: 0.1667 - dense_1_acc_4: 0.2000 - dense_1_acc_5: 0.2333 - dense_1_acc_6: 0.1000 - dense_1_acc_7: 0.1667 - dense_1_acc_8: 0.2167 - dense_1_acc_9: 0.1333 - dense_1_acc_10: 0.2000 - dense_1_acc_11: 0.1833 - dense_1_acc_12: 0.1333 - dense_1_acc_13: 0.2500 - dense_1_acc_14: 0.2000 - dense_1_acc_15: 0.1500 - dense_1_acc_16: 0.2000 - dense_1_acc_17: 0.1667 - dense_1_acc_18: 0.0833 - dense_1_acc_19: 0.2000 - dense_1_acc_20: 0.0833 - dense_1_acc_21: 0.1000 - dense_1_acc_22: 0.1333 - dense_1_acc_23: 0.2167 - dense_1_acc_24: 0.1000 - dense_1_acc_25: 0.0833 - dense_1_acc_26: 0.1500 - dense_1_acc_27: 0.1333 - dense_1_acc_28: 0.1667 - dense_1_acc_29: 0.0667 - dense_1_acc_30: 0.0000e+00         
Epoch 3/100
60/60 [==============================] - 0s - loss: 115.8623 - dense_1_loss_1: 4.3145 - dense_1_loss_2: 4.2642 - dense_1_loss_3: 4.2006 - dense_1_loss_4: 4.1763 - dense_1_loss_5: 4.1281 - dense_1_loss_6: 4.1364 - dense_1_loss_7: 4.0949 - dense_1_loss_8: 4.0109 - dense_1_loss_9: 4.0096 - dense_1_loss_10: 3.8945 - dense_1_loss_11: 3.9074 - dense_1_loss_12: 3.9683 - dense_1_loss_13: 3.8582 - dense_1_loss_14: 3.8621 - dense_1_loss_15: 3.9187 - dense_1_loss_16: 3.9111 - dense_1_loss_17: 3.9448 - dense_1_loss_18: 4.0755 - dense_1_loss_19: 3.7453 - dense_1_loss_20: 4.0050 - dense_1_loss_21: 4.0289 - dense_1_loss_22: 3.9408 - dense_1_loss_23: 3.8178 - dense_1_loss_24: 3.9313 - dense_1_loss_25: 4.0872 - dense_1_loss_26: 3.7345 - dense_1_loss_27: 3.9727 - dense_1_loss_28: 3.8958 - dense_1_loss_29: 4.0270 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0333 - dense_1_acc_2: 0.1000 - dense_1_acc_3: 0.2000 - dense_1_acc_4: 0.2167 - dense_1_acc_5: 0.2000 - dense_1_acc_6: 0.0667 - dense_1_acc_7: 0.1167 - dense_1_acc_8: 0.1833 - dense_1_acc_9: 0.1667 - dense_1_acc_10: 0.1167 - dense_1_acc_11: 0.1667 - dense_1_acc_12: 0.1000 - dense_1_acc_13: 0.2167 - dense_1_acc_14: 0.1667 - dense_1_acc_15: 0.1500 - dense_1_acc_16: 0.1167 - dense_1_acc_17: 0.1333 - dense_1_acc_18: 0.0833 - dense_1_acc_19: 0.1500 - dense_1_acc_20: 0.0500 - dense_1_acc_21: 0.0833 - dense_1_acc_22: 0.1333 - dense_1_acc_23: 0.1500 - dense_1_acc_24: 0.1000 - dense_1_acc_25: 0.0500 - dense_1_acc_26: 0.1167 - dense_1_acc_27: 0.1167 - dense_1_acc_28: 0.1000 - dense_1_acc_29: 0.0333 - dense_1_acc_30: 0.0000e+00         
Epoch 4/100
60/60 [==============================] - 0s - loss: 113.6339 - dense_1_loss_1: 4.2927 - dense_1_loss_2: 4.2178 - dense_1_loss_3: 4.1111 - dense_1_loss_4: 4.0723 - dense_1_loss_5: 3.9783 - dense_1_loss_6: 3.9924 - dense_1_loss_7: 3.9273 - dense_1_loss_8: 3.7339 - dense_1_loss_9: 3.8267 - dense_1_loss_10: 3.6391 - dense_1_loss_11: 3.7734 - dense_1_loss_12: 3.9426 - dense_1_loss_13: 3.7747 - dense_1_loss_14: 3.6864 - dense_1_loss_15: 3.7333 - dense_1_loss_16: 3.8419 - dense_1_loss_17: 3.9879 - dense_1_loss_18: 3.9871 - dense_1_loss_19: 3.7347 - dense_1_loss_20: 4.0957 - dense_1_loss_21: 4.0762 - dense_1_loss_22: 3.9390 - dense_1_loss_23: 3.9603 - dense_1_loss_24: 3.9039 - dense_1_loss_25: 4.0061 - dense_1_loss_26: 3.7409 - dense_1_loss_27: 3.7853 - dense_1_loss_28: 3.8825 - dense_1_loss_29: 3.9906 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1000 - dense_1_acc_3: 0.2167 - dense_1_acc_4: 0.2167 - dense_1_acc_5: 0.2667 - dense_1_acc_6: 0.1000 - dense_1_acc_7: 0.1167 - dense_1_acc_8: 0.2167 - dense_1_acc_9: 0.1500 - dense_1_acc_10: 0.1333 - dense_1_acc_11: 0.1167 - dense_1_acc_12: 0.1167 - dense_1_acc_13: 0.1667 - dense_1_acc_14: 0.1333 - dense_1_acc_15: 0.1000 - dense_1_acc_16: 0.0833 - dense_1_acc_17: 0.1333 - dense_1_acc_18: 0.0500 - dense_1_acc_19: 0.1000 - dense_1_acc_20: 0.1000 - dense_1_acc_21: 0.0833 - dense_1_acc_22: 0.0333 - dense_1_acc_23: 0.0333 - dense_1_acc_24: 0.0167 - dense_1_acc_25: 0.1000 - dense_1_acc_26: 0.1167 - dense_1_acc_27: 0.0833 - dense_1_acc_28: 0.0833 - dense_1_acc_29: 0.1333 - dense_1_acc_30: 0.0000e+00         
Epoch 5/100
60/60 [==============================] - 0s - loss: 109.7924 - dense_1_loss_1: 4.2760 - dense_1_loss_2: 4.1811 - dense_1_loss_3: 4.0439 - dense_1_loss_4: 4.0045 - dense_1_loss_5: 3.8902 - dense_1_loss_6: 3.9189 - dense_1_loss_7: 3.8213 - dense_1_loss_8: 3.6436 - dense_1_loss_9: 3.7225 - dense_1_loss_10: 3.4904 - dense_1_loss_11: 3.6176 - dense_1_loss_12: 3.9742 - dense_1_loss_13: 3.6933 - dense_1_loss_14: 3.5247 - dense_1_loss_15: 3.6456 - dense_1_loss_16: 3.6501 - dense_1_loss_17: 3.7455 - dense_1_loss_18: 3.7858 - dense_1_loss_19: 3.5062 - dense_1_loss_20: 3.8581 - dense_1_loss_21: 3.8773 - dense_1_loss_22: 3.7139 - dense_1_loss_23: 3.6176 - dense_1_loss_24: 3.6223 - dense_1_loss_25: 3.9796 - dense_1_loss_26: 3.5425 - dense_1_loss_27: 3.7442 - dense_1_loss_28: 3.7502 - dense_1_loss_29: 3.9512 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1000 - dense_1_acc_3: 0.2167 - dense_1_acc_4: 0.2167 - dense_1_acc_5: 0.1167 - dense_1_acc_6: 0.0667 - dense_1_acc_7: 0.1000 - dense_1_acc_8: 0.1500 - dense_1_acc_9: 0.1667 - dense_1_acc_10: 0.1167 - dense_1_acc_11: 0.1333 - dense_1_acc_12: 0.0333 - dense_1_acc_13: 0.1333 - dense_1_acc_14: 0.1167 - dense_1_acc_15: 0.1500 - dense_1_acc_16: 0.1167 - dense_1_acc_17: 0.1500 - dense_1_acc_18: 0.0667 - dense_1_acc_19: 0.1167 - dense_1_acc_20: 0.1000 - dense_1_acc_21: 0.1000 - dense_1_acc_22: 0.0833 - dense_1_acc_23: 0.1833 - dense_1_acc_24: 0.1333 - dense_1_acc_25: 0.0333 - dense_1_acc_26: 0.1667 - dense_1_acc_27: 0.1500 - dense_1_acc_28: 0.1167 - dense_1_acc_29: 0.0500 - dense_1_acc_30: 0.0000e+00         
Epoch 6/100
60/60 [==============================] - 0s - loss: 106.6430 - dense_1_loss_1: 4.2631 - dense_1_loss_2: 4.1534 - dense_1_loss_3: 3.9931 - dense_1_loss_4: 3.9514 - dense_1_loss_5: 3.8498 - dense_1_loss_6: 3.8644 - dense_1_loss_7: 3.7746 - dense_1_loss_8: 3.5834 - dense_1_loss_9: 3.6210 - dense_1_loss_10: 3.4681 - dense_1_loss_11: 3.5333 - dense_1_loss_12: 3.8251 - dense_1_loss_13: 3.5653 - dense_1_loss_14: 3.3611 - dense_1_loss_15: 3.4985 - dense_1_loss_16: 3.5461 - dense_1_loss_17: 3.6197 - dense_1_loss_18: 3.5817 - dense_1_loss_19: 3.3937 - dense_1_loss_20: 3.6840 - dense_1_loss_21: 3.6969 - dense_1_loss_22: 3.5848 - dense_1_loss_23: 3.4989 - dense_1_loss_24: 3.5386 - dense_1_loss_25: 3.7711 - dense_1_loss_26: 3.4608 - dense_1_loss_27: 3.5670 - dense_1_loss_28: 3.5948 - dense_1_loss_29: 3.7989 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0833 - dense_1_acc_2: 0.1167 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2500 - dense_1_acc_5: 0.2167 - dense_1_acc_6: 0.0667 - dense_1_acc_7: 0.1333 - dense_1_acc_8: 0.2000 - dense_1_acc_9: 0.2167 - dense_1_acc_10: 0.1667 - dense_1_acc_11: 0.1167 - dense_1_acc_12: 0.0667 - dense_1_acc_13: 0.1833 - dense_1_acc_14: 0.1667 - dense_1_acc_15: 0.1833 - dense_1_acc_16: 0.1000 - dense_1_acc_17: 0.1333 - dense_1_acc_18: 0.0833 - dense_1_acc_19: 0.1500 - dense_1_acc_20: 0.0833 - dense_1_acc_21: 0.1000 - dense_1_acc_22: 0.1333 - dense_1_acc_23: 0.1833 - dense_1_acc_24: 0.1667 - dense_1_acc_25: 0.0500 - dense_1_acc_26: 0.2167 - dense_1_acc_27: 0.1333 - dense_1_acc_28: 0.1667 - dense_1_acc_29: 0.0500 - dense_1_acc_30: 0.0000e+00     
Epoch 7/100
60/60 [==============================] - 0s - loss: 103.5522 - dense_1_loss_1: 4.2499 - dense_1_loss_2: 4.1253 - dense_1_loss_3: 3.9393 - dense_1_loss_4: 3.8993 - dense_1_loss_5: 3.7899 - dense_1_loss_6: 3.7947 - dense_1_loss_7: 3.7200 - dense_1_loss_8: 3.5007 - dense_1_loss_9: 3.5212 - dense_1_loss_10: 3.3557 - dense_1_loss_11: 3.4555 - dense_1_loss_12: 3.6894 - dense_1_loss_13: 3.4037 - dense_1_loss_14: 3.2587 - dense_1_loss_15: 3.4038 - dense_1_loss_16: 3.4396 - dense_1_loss_17: 3.4393 - dense_1_loss_18: 3.4420 - dense_1_loss_19: 3.2950 - dense_1_loss_20: 3.5190 - dense_1_loss_21: 3.5493 - dense_1_loss_22: 3.4382 - dense_1_loss_23: 3.4019 - dense_1_loss_24: 3.3991 - dense_1_loss_25: 3.7193 - dense_1_loss_26: 3.2772 - dense_1_loss_27: 3.4608 - dense_1_loss_28: 3.4207 - dense_1_loss_29: 3.6439 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0333 - dense_1_acc_2: 0.1333 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2500 - dense_1_acc_5: 0.2667 - dense_1_acc_6: 0.0833 - dense_1_acc_7: 0.1500 - dense_1_acc_8: 0.2333 - dense_1_acc_9: 0.2167 - dense_1_acc_10: 0.2000 - dense_1_acc_11: 0.1500 - dense_1_acc_12: 0.0500 - dense_1_acc_13: 0.2000 - dense_1_acc_14: 0.2000 - dense_1_acc_15: 0.1833 - dense_1_acc_16: 0.1167 - dense_1_acc_17: 0.1833 - dense_1_acc_18: 0.0833 - dense_1_acc_19: 0.1500 - dense_1_acc_20: 0.1000 - dense_1_acc_21: 0.0667 - dense_1_acc_22: 0.1333 - dense_1_acc_23: 0.1667 - dense_1_acc_24: 0.1667 - dense_1_acc_25: 0.0667 - dense_1_acc_26: 0.2167 - dense_1_acc_27: 0.1833 - dense_1_acc_28: 0.1500 - dense_1_acc_29: 0.0500 - dense_1_acc_30: 0.0000e+00         
Epoch 8/100
60/60 [==============================] - 0s - loss: 100.3082 - dense_1_loss_1: 4.2368 - dense_1_loss_2: 4.0906 - dense_1_loss_3: 3.8777 - dense_1_loss_4: 3.8319 - dense_1_loss_5: 3.7071 - dense_1_loss_6: 3.7056 - dense_1_loss_7: 3.6443 - dense_1_loss_8: 3.3982 - dense_1_loss_9: 3.3998 - dense_1_loss_10: 3.2259 - dense_1_loss_11: 3.3601 - dense_1_loss_12: 3.5492 - dense_1_loss_13: 3.2635 - dense_1_loss_14: 3.1483 - dense_1_loss_15: 3.2772 - dense_1_loss_16: 3.3336 - dense_1_loss_17: 3.2830 - dense_1_loss_18: 3.2965 - dense_1_loss_19: 3.2035 - dense_1_loss_20: 3.3919 - dense_1_loss_21: 3.3994 - dense_1_loss_22: 3.2814 - dense_1_loss_23: 3.2755 - dense_1_loss_24: 3.2992 - dense_1_loss_25: 3.5954 - dense_1_loss_26: 3.1469 - dense_1_loss_27: 3.2738 - dense_1_loss_28: 3.3398 - dense_1_loss_29: 3.4722 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1667 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2167 - dense_1_acc_5: 0.2667 - dense_1_acc_6: 0.1000 - dense_1_acc_7: 0.1667 - dense_1_acc_8: 0.2500 - dense_1_acc_9: 0.2167 - dense_1_acc_10: 0.2167 - dense_1_acc_11: 0.2000 - dense_1_acc_12: 0.1000 - dense_1_acc_13: 0.2667 - dense_1_acc_14: 0.2500 - dense_1_acc_15: 0.1667 - dense_1_acc_16: 0.1333 - dense_1_acc_17: 0.2500 - dense_1_acc_18: 0.1167 - dense_1_acc_19: 0.2000 - dense_1_acc_20: 0.1500 - dense_1_acc_21: 0.1667 - dense_1_acc_22: 0.1667 - dense_1_acc_23: 0.1500 - dense_1_acc_24: 0.1833 - dense_1_acc_25: 0.1000 - dense_1_acc_26: 0.3000 - dense_1_acc_27: 0.1833 - dense_1_acc_28: 0.1667 - dense_1_acc_29: 0.1500 - dense_1_acc_30: 0.0000e+00     
Epoch 9/100
60/60 [==============================] - 0s - loss: 96.6887 - dense_1_loss_1: 4.2251 - dense_1_loss_2: 4.0534 - dense_1_loss_3: 3.8075 - dense_1_loss_4: 3.7563 - dense_1_loss_5: 3.6018 - dense_1_loss_6: 3.5958 - dense_1_loss_7: 3.5586 - dense_1_loss_8: 3.2848 - dense_1_loss_9: 3.2645 - dense_1_loss_10: 3.1209 - dense_1_loss_11: 3.2311 - dense_1_loss_12: 3.3981 - dense_1_loss_13: 3.0904 - dense_1_loss_14: 2.9840 - dense_1_loss_15: 3.1699 - dense_1_loss_16: 3.2213 - dense_1_loss_17: 3.1242 - dense_1_loss_18: 3.1612 - dense_1_loss_19: 3.0500 - dense_1_loss_20: 3.2121 - dense_1_loss_21: 3.2398 - dense_1_loss_22: 3.1252 - dense_1_loss_23: 3.1720 - dense_1_loss_24: 3.1800 - dense_1_loss_25: 3.4527 - dense_1_loss_26: 2.9610 - dense_1_loss_27: 3.1499 - dense_1_loss_28: 3.1976 - dense_1_loss_29: 3.2996 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2167 - dense_1_acc_3: 0.2333 - dense_1_acc_4: 0.2000 - dense_1_acc_5: 0.3167 - dense_1_acc_6: 0.1333 - dense_1_acc_7: 0.1667 - dense_1_acc_8: 0.2000 - dense_1_acc_9: 0.2167 - dense_1_acc_10: 0.2333 - dense_1_acc_11: 0.2000 - dense_1_acc_12: 0.1167 - dense_1_acc_13: 0.2833 - dense_1_acc_14: 0.3167 - dense_1_acc_15: 0.1833 - dense_1_acc_16: 0.1833 - dense_1_acc_17: 0.2833 - dense_1_acc_18: 0.1667 - dense_1_acc_19: 0.2167 - dense_1_acc_20: 0.2667 - dense_1_acc_21: 0.1833 - dense_1_acc_22: 0.1500 - dense_1_acc_23: 0.1667 - dense_1_acc_24: 0.1833 - dense_1_acc_25: 0.1333 - dense_1_acc_26: 0.2833 - dense_1_acc_27: 0.1667 - dense_1_acc_28: 0.2000 - dense_1_acc_29: 0.2000 - dense_1_acc_30: 0.0000e+00     
Epoch 10/100
60/60 [==============================] - 0s - loss: 92.8527 - dense_1_loss_1: 4.2137 - dense_1_loss_2: 4.0150 - dense_1_loss_3: 3.7343 - dense_1_loss_4: 3.6692 - dense_1_loss_5: 3.4880 - dense_1_loss_6: 3.4764 - dense_1_loss_7: 3.4381 - dense_1_loss_8: 3.1455 - dense_1_loss_9: 3.1080 - dense_1_loss_10: 2.9837 - dense_1_loss_11: 3.1136 - dense_1_loss_12: 3.2155 - dense_1_loss_13: 2.9173 - dense_1_loss_14: 2.8603 - dense_1_loss_15: 2.9930 - dense_1_loss_16: 3.0500 - dense_1_loss_17: 2.9130 - dense_1_loss_18: 2.9704 - dense_1_loss_19: 2.9851 - dense_1_loss_20: 3.0968 - dense_1_loss_21: 3.1286 - dense_1_loss_22: 2.9848 - dense_1_loss_23: 3.0264 - dense_1_loss_24: 3.0262 - dense_1_loss_25: 3.3032 - dense_1_loss_26: 2.7726 - dense_1_loss_27: 3.0326 - dense_1_loss_28: 3.0475 - dense_1_loss_29: 3.1439 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2500 - dense_1_acc_3: 0.2333 - dense_1_acc_4: 0.1833 - dense_1_acc_5: 0.3000 - dense_1_acc_6: 0.1333 - dense_1_acc_7: 0.1167 - dense_1_acc_8: 0.1500 - dense_1_acc_9: 0.2167 - dense_1_acc_10: 0.2167 - dense_1_acc_11: 0.1833 - dense_1_acc_12: 0.1167 - dense_1_acc_13: 0.2500 - dense_1_acc_14: 0.2667 - dense_1_acc_15: 0.2000 - dense_1_acc_16: 0.1833 - dense_1_acc_17: 0.2833 - dense_1_acc_18: 0.1667 - dense_1_acc_19: 0.2167 - dense_1_acc_20: 0.2500 - dense_1_acc_21: 0.1833 - dense_1_acc_22: 0.1833 - dense_1_acc_23: 0.2000 - dense_1_acc_24: 0.1833 - dense_1_acc_25: 0.1333 - dense_1_acc_26: 0.2833 - dense_1_acc_27: 0.1333 - dense_1_acc_28: 0.2667 - dense_1_acc_29: 0.1833 - dense_1_acc_30: 0.0000e+00     
Epoch 11/100
60/60 [==============================] - 0s - loss: 89.0018 - dense_1_loss_1: 4.2032 - dense_1_loss_2: 3.9777 - dense_1_loss_3: 3.6646 - dense_1_loss_4: 3.5779 - dense_1_loss_5: 3.3766 - dense_1_loss_6: 3.3386 - dense_1_loss_7: 3.2988 - dense_1_loss_8: 2.9885 - dense_1_loss_9: 2.9699 - dense_1_loss_10: 2.8495 - dense_1_loss_11: 2.9809 - dense_1_loss_12: 3.0366 - dense_1_loss_13: 2.7671 - dense_1_loss_14: 2.7231 - dense_1_loss_15: 2.8212 - dense_1_loss_16: 2.9106 - dense_1_loss_17: 2.7385 - dense_1_loss_18: 2.8308 - dense_1_loss_19: 2.8391 - dense_1_loss_20: 2.9368 - dense_1_loss_21: 2.9781 - dense_1_loss_22: 2.8563 - dense_1_loss_23: 2.9326 - dense_1_loss_24: 2.8747 - dense_1_loss_25: 3.1629 - dense_1_loss_26: 2.6144 - dense_1_loss_27: 2.9071 - dense_1_loss_28: 2.8815 - dense_1_loss_29: 2.9644 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2667 - dense_1_acc_3: 0.2167 - dense_1_acc_4: 0.1833 - dense_1_acc_5: 0.2667 - dense_1_acc_6: 0.1000 - dense_1_acc_7: 0.1333 - dense_1_acc_8: 0.1667 - dense_1_acc_9: 0.2333 - dense_1_acc_10: 0.2000 - dense_1_acc_11: 0.1667 - dense_1_acc_12: 0.1333 - dense_1_acc_13: 0.3000 - dense_1_acc_14: 0.3000 - dense_1_acc_15: 0.2333 - dense_1_acc_16: 0.1833 - dense_1_acc_17: 0.2833 - dense_1_acc_18: 0.1667 - dense_1_acc_19: 0.2333 - dense_1_acc_20: 0.1667 - dense_1_acc_21: 0.1333 - dense_1_acc_22: 0.2333 - dense_1_acc_23: 0.1833 - dense_1_acc_24: 0.1833 - dense_1_acc_25: 0.2000 - dense_1_acc_26: 0.3167 - dense_1_acc_27: 0.1833 - dense_1_acc_28: 0.2667 - dense_1_acc_29: 0.2167 - dense_1_acc_30: 0.0000e+00     
Epoch 12/100
60/60 [==============================] - 0s - loss: 84.7570 - dense_1_loss_1: 4.1943 - dense_1_loss_2: 3.9399 - dense_1_loss_3: 3.5894 - dense_1_loss_4: 3.4842 - dense_1_loss_5: 3.2570 - dense_1_loss_6: 3.1976 - dense_1_loss_7: 3.1560 - dense_1_loss_8: 2.8515 - dense_1_loss_9: 2.8474 - dense_1_loss_10: 2.7538 - dense_1_loss_11: 2.8616 - dense_1_loss_12: 2.8403 - dense_1_loss_13: 2.6298 - dense_1_loss_14: 2.5892 - dense_1_loss_15: 2.7159 - dense_1_loss_16: 2.8232 - dense_1_loss_17: 2.5801 - dense_1_loss_18: 2.6497 - dense_1_loss_19: 2.6282 - dense_1_loss_20: 2.6917 - dense_1_loss_21: 2.7601 - dense_1_loss_22: 2.6781 - dense_1_loss_23: 2.7417 - dense_1_loss_24: 2.7022 - dense_1_loss_25: 2.9451 - dense_1_loss_26: 2.4747 - dense_1_loss_27: 2.6336 - dense_1_loss_28: 2.7227 - dense_1_loss_29: 2.8181 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2167 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.1833 - dense_1_acc_5: 0.3000 - dense_1_acc_6: 0.1000 - dense_1_acc_7: 0.1333 - dense_1_acc_8: 0.1833 - dense_1_acc_9: 0.1833 - dense_1_acc_10: 0.2000 - dense_1_acc_11: 0.1833 - dense_1_acc_12: 0.1167 - dense_1_acc_13: 0.3167 - dense_1_acc_14: 0.3000 - dense_1_acc_15: 0.2333 - dense_1_acc_16: 0.2000 - dense_1_acc_17: 0.2500 - dense_1_acc_18: 0.1833 - dense_1_acc_19: 0.2833 - dense_1_acc_20: 0.2333 - dense_1_acc_21: 0.2000 - dense_1_acc_22: 0.2500 - dense_1_acc_23: 0.2167 - dense_1_acc_24: 0.1833 - dense_1_acc_25: 0.1667 - dense_1_acc_26: 0.3000 - dense_1_acc_27: 0.2000 - dense_1_acc_28: 0.2667 - dense_1_acc_29: 0.2500 - dense_1_acc_30: 0.0000e+00     
Epoch 13/100
60/60 [==============================] - 0s - loss: 81.9873 - dense_1_loss_1: 4.1844 - dense_1_loss_2: 3.9013 - dense_1_loss_3: 3.5112 - dense_1_loss_4: 3.3823 - dense_1_loss_5: 3.1384 - dense_1_loss_6: 3.0567 - dense_1_loss_7: 2.9954 - dense_1_loss_8: 2.6625 - dense_1_loss_9: 2.7144 - dense_1_loss_10: 2.6402 - dense_1_loss_11: 2.7691 - dense_1_loss_12: 2.7686 - dense_1_loss_13: 2.5644 - dense_1_loss_14: 2.5289 - dense_1_loss_15: 2.5688 - dense_1_loss_16: 2.7000 - dense_1_loss_17: 2.5005 - dense_1_loss_18: 2.5096 - dense_1_loss_19: 2.5661 - dense_1_loss_20: 2.6370 - dense_1_loss_21: 2.7080 - dense_1_loss_22: 2.6204 - dense_1_loss_23: 2.5707 - dense_1_loss_24: 2.6439 - dense_1_loss_25: 2.8190 - dense_1_loss_26: 2.3580 - dense_1_loss_27: 2.5941 - dense_1_loss_28: 2.6045 - dense_1_loss_29: 2.7688 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1833 - dense_1_acc_3: 0.3167 - dense_1_acc_4: 0.2000 - dense_1_acc_5: 0.3000 - dense_1_acc_6: 0.1500 - dense_1_acc_7: 0.1500 - dense_1_acc_8: 0.3333 - dense_1_acc_9: 0.2333 - dense_1_acc_10: 0.2167 - dense_1_acc_11: 0.2333 - dense_1_acc_12: 0.1500 - dense_1_acc_13: 0.2667 - dense_1_acc_14: 0.3000 - dense_1_acc_15: 0.2500 - dense_1_acc_16: 0.1833 - dense_1_acc_17: 0.2500 - dense_1_acc_18: 0.2667 - dense_1_acc_19: 0.3000 - dense_1_acc_20: 0.2833 - dense_1_acc_21: 0.1833 - dense_1_acc_22: 0.1833 - dense_1_acc_23: 0.2833 - dense_1_acc_24: 0.2167 - dense_1_acc_25: 0.1833 - dense_1_acc_26: 0.3333 - dense_1_acc_27: 0.2000 - dense_1_acc_28: 0.3167 - dense_1_acc_29: 0.1833 - dense_1_acc_30: 0.0000e+00     
Epoch 14/100
60/60 [==============================] - 0s - loss: 78.1189 - dense_1_loss_1: 4.1782 - dense_1_loss_2: 3.8660 - dense_1_loss_3: 3.4401 - dense_1_loss_4: 3.2829 - dense_1_loss_5: 3.0256 - dense_1_loss_6: 2.9191 - dense_1_loss_7: 2.8835 - dense_1_loss_8: 2.5557 - dense_1_loss_9: 2.6147 - dense_1_loss_10: 2.5415 - dense_1_loss_11: 2.6313 - dense_1_loss_12: 2.5516 - dense_1_loss_13: 2.3550 - dense_1_loss_14: 2.3506 - dense_1_loss_15: 2.4316 - dense_1_loss_16: 2.5041 - dense_1_loss_17: 2.3479 - dense_1_loss_18: 2.3484 - dense_1_loss_19: 2.3500 - dense_1_loss_20: 2.4835 - dense_1_loss_21: 2.5455 - dense_1_loss_22: 2.4080 - dense_1_loss_23: 2.4770 - dense_1_loss_24: 2.4663 - dense_1_loss_25: 2.7300 - dense_1_loss_26: 2.2946 - dense_1_loss_27: 2.4445 - dense_1_loss_28: 2.4703 - dense_1_loss_29: 2.6213 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1333 - dense_1_acc_3: 0.3167 - dense_1_acc_4: 0.2500 - dense_1_acc_5: 0.3500 - dense_1_acc_6: 0.1833 - dense_1_acc_7: 0.2333 - dense_1_acc_8: 0.3500 - dense_1_acc_9: 0.2500 - dense_1_acc_10: 0.2500 - dense_1_acc_11: 0.2833 - dense_1_acc_12: 0.2167 - dense_1_acc_13: 0.3667 - dense_1_acc_14: 0.3167 - dense_1_acc_15: 0.3167 - dense_1_acc_16: 0.2667 - dense_1_acc_17: 0.2167 - dense_1_acc_18: 0.3333 - dense_1_acc_19: 0.3500 - dense_1_acc_20: 0.2667 - dense_1_acc_21: 0.1667 - dense_1_acc_22: 0.3000 - dense_1_acc_23: 0.3000 - dense_1_acc_24: 0.2000 - dense_1_acc_25: 0.1167 - dense_1_acc_26: 0.3333 - dense_1_acc_27: 0.3000 - dense_1_acc_28: 0.3167 - dense_1_acc_29: 0.2167 - dense_1_acc_30: 0.0000e+00     
Epoch 15/100
60/60 [==============================] - 0s - loss: 74.4305 - dense_1_loss_1: 4.1706 - dense_1_loss_2: 3.8297 - dense_1_loss_3: 3.3647 - dense_1_loss_4: 3.1996 - dense_1_loss_5: 2.9173 - dense_1_loss_6: 2.7958 - dense_1_loss_7: 2.7501 - dense_1_loss_8: 2.4633 - dense_1_loss_9: 2.5038 - dense_1_loss_10: 2.4565 - dense_1_loss_11: 2.4821 - dense_1_loss_12: 2.4464 - dense_1_loss_13: 2.2691 - dense_1_loss_14: 2.2693 - dense_1_loss_15: 2.2822 - dense_1_loss_16: 2.4620 - dense_1_loss_17: 2.2595 - dense_1_loss_18: 2.2699 - dense_1_loss_19: 2.3021 - dense_1_loss_20: 2.2933 - dense_1_loss_21: 2.3828 - dense_1_loss_22: 2.3079 - dense_1_loss_23: 2.2539 - dense_1_loss_24: 2.2474 - dense_1_loss_25: 2.4733 - dense_1_loss_26: 2.0910 - dense_1_loss_27: 2.2538 - dense_1_loss_28: 2.2881 - dense_1_loss_29: 2.3450 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1333 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2833 - dense_1_acc_5: 0.3667 - dense_1_acc_6: 0.2500 - dense_1_acc_7: 0.2000 - dense_1_acc_8: 0.3667 - dense_1_acc_9: 0.3667 - dense_1_acc_10: 0.3167 - dense_1_acc_11: 0.2833 - dense_1_acc_12: 0.2667 - dense_1_acc_13: 0.4000 - dense_1_acc_14: 0.4333 - dense_1_acc_15: 0.3833 - dense_1_acc_16: 0.2500 - dense_1_acc_17: 0.3167 - dense_1_acc_18: 0.3333 - dense_1_acc_19: 0.3333 - dense_1_acc_20: 0.2500 - dense_1_acc_21: 0.2333 - dense_1_acc_22: 0.2333 - dense_1_acc_23: 0.3500 - dense_1_acc_24: 0.2167 - dense_1_acc_25: 0.1667 - dense_1_acc_26: 0.4500 - dense_1_acc_27: 0.3333 - dense_1_acc_28: 0.3167 - dense_1_acc_29: 0.3333 - dense_1_acc_30: 0.0000e+00     
Epoch 16/100
60/60 [==============================] - 0s - loss: 70.5487 - dense_1_loss_1: 4.1626 - dense_1_loss_2: 3.7915 - dense_1_loss_3: 3.2828 - dense_1_loss_4: 3.1006 - dense_1_loss_5: 2.8012 - dense_1_loss_6: 2.6628 - dense_1_loss_7: 2.6160 - dense_1_loss_8: 2.3129 - dense_1_loss_9: 2.3921 - dense_1_loss_10: 2.3498 - dense_1_loss_11: 2.3059 - dense_1_loss_12: 2.2605 - dense_1_loss_13: 2.1157 - dense_1_loss_14: 2.0692 - dense_1_loss_15: 2.1119 - dense_1_loss_16: 2.2639 - dense_1_loss_17: 2.1048 - dense_1_loss_18: 2.1349 - dense_1_loss_19: 2.0590 - dense_1_loss_20: 2.1220 - dense_1_loss_21: 2.1921 - dense_1_loss_22: 2.1090 - dense_1_loss_23: 2.1002 - dense_1_loss_24: 2.2401 - dense_1_loss_25: 2.4336 - dense_1_loss_26: 2.0067 - dense_1_loss_27: 2.1415 - dense_1_loss_28: 2.1097 - dense_1_loss_29: 2.1957 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1333 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2667 - dense_1_acc_5: 0.3333 - dense_1_acc_6: 0.3500 - dense_1_acc_7: 0.2500 - dense_1_acc_8: 0.3667 - dense_1_acc_9: 0.3500 - dense_1_acc_10: 0.3000 - dense_1_acc_11: 0.3833 - dense_1_acc_12: 0.2333 - dense_1_acc_13: 0.4167 - dense_1_acc_14: 0.4333 - dense_1_acc_15: 0.3167 - dense_1_acc_16: 0.2500 - dense_1_acc_17: 0.3000 - dense_1_acc_18: 0.3500 - dense_1_acc_19: 0.4000 - dense_1_acc_20: 0.3500 - dense_1_acc_21: 0.2500 - dense_1_acc_22: 0.2833 - dense_1_acc_23: 0.3667 - dense_1_acc_24: 0.2500 - dense_1_acc_25: 0.1500 - dense_1_acc_26: 0.3667 - dense_1_acc_27: 0.3833 - dense_1_acc_28: 0.3333 - dense_1_acc_29: 0.3667 - dense_1_acc_30: 0.0000e+00     
Epoch 17/100
60/60 [==============================] - 0s - loss: 67.2444 - dense_1_loss_1: 4.1540 - dense_1_loss_2: 3.7495 - dense_1_loss_3: 3.1984 - dense_1_loss_4: 3.0009 - dense_1_loss_5: 2.6881 - dense_1_loss_6: 2.5310 - dense_1_loss_7: 2.4602 - dense_1_loss_8: 2.1910 - dense_1_loss_9: 2.2705 - dense_1_loss_10: 2.2331 - dense_1_loss_11: 2.1755 - dense_1_loss_12: 2.0916 - dense_1_loss_13: 2.0020 - dense_1_loss_14: 1.9309 - dense_1_loss_15: 2.0047 - dense_1_loss_16: 2.0856 - dense_1_loss_17: 2.0029 - dense_1_loss_18: 2.0613 - dense_1_loss_19: 1.9371 - dense_1_loss_20: 1.9916 - dense_1_loss_21: 2.0263 - dense_1_loss_22: 2.0219 - dense_1_loss_23: 2.0479 - dense_1_loss_24: 2.1525 - dense_1_loss_25: 2.2799 - dense_1_loss_26: 1.9147 - dense_1_loss_27: 2.0434 - dense_1_loss_28: 1.9729 - dense_1_loss_29: 2.0250 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1333 - dense_1_acc_3: 0.3167 - dense_1_acc_4: 0.2833 - dense_1_acc_5: 0.3167 - dense_1_acc_6: 0.3833 - dense_1_acc_7: 0.3833 - dense_1_acc_8: 0.4333 - dense_1_acc_9: 0.3833 - dense_1_acc_10: 0.3333 - dense_1_acc_11: 0.3833 - dense_1_acc_12: 0.2667 - dense_1_acc_13: 0.4000 - dense_1_acc_14: 0.3833 - dense_1_acc_15: 0.3667 - dense_1_acc_16: 0.2667 - dense_1_acc_17: 0.3167 - dense_1_acc_18: 0.2667 - dense_1_acc_19: 0.4500 - dense_1_acc_20: 0.4000 - dense_1_acc_21: 0.3333 - dense_1_acc_22: 0.3167 - dense_1_acc_23: 0.4500 - dense_1_acc_24: 0.3167 - dense_1_acc_25: 0.2000 - dense_1_acc_26: 0.4167 - dense_1_acc_27: 0.4333 - dense_1_acc_28: 0.4000 - dense_1_acc_29: 0.4333 - dense_1_acc_30: 0.0000e+00     
Epoch 18/100
60/60 [==============================] - 0s - loss: 64.3881 - dense_1_loss_1: 4.1443 - dense_1_loss_2: 3.7036 - dense_1_loss_3: 3.1149 - dense_1_loss_4: 2.8925 - dense_1_loss_5: 2.5764 - dense_1_loss_6: 2.4210 - dense_1_loss_7: 2.3237 - dense_1_loss_8: 2.0600 - dense_1_loss_9: 2.1509 - dense_1_loss_10: 2.1572 - dense_1_loss_11: 2.1187 - dense_1_loss_12: 2.0009 - dense_1_loss_13: 1.8716 - dense_1_loss_14: 1.8213 - dense_1_loss_15: 1.9668 - dense_1_loss_16: 1.9758 - dense_1_loss_17: 1.9644 - dense_1_loss_18: 1.9701 - dense_1_loss_19: 1.8826 - dense_1_loss_20: 1.9309 - dense_1_loss_21: 1.9211 - dense_1_loss_22: 1.9390 - dense_1_loss_23: 1.9123 - dense_1_loss_24: 2.0139 - dense_1_loss_25: 2.1316 - dense_1_loss_26: 1.8311 - dense_1_loss_27: 1.8508 - dense_1_loss_28: 1.8342 - dense_1_loss_29: 1.9064 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1667 - dense_1_acc_3: 0.3167 - dense_1_acc_4: 0.2667 - dense_1_acc_5: 0.3167 - dense_1_acc_6: 0.4000 - dense_1_acc_7: 0.3833 - dense_1_acc_8: 0.4167 - dense_1_acc_9: 0.4333 - dense_1_acc_10: 0.3667 - dense_1_acc_11: 0.4333 - dense_1_acc_12: 0.3000 - dense_1_acc_13: 0.4333 - dense_1_acc_14: 0.4833 - dense_1_acc_15: 0.3500 - dense_1_acc_16: 0.3167 - dense_1_acc_17: 0.2833 - dense_1_acc_18: 0.3667 - dense_1_acc_19: 0.4667 - dense_1_acc_20: 0.4000 - dense_1_acc_21: 0.4333 - dense_1_acc_22: 0.4833 - dense_1_acc_23: 0.4167 - dense_1_acc_24: 0.3500 - dense_1_acc_25: 0.2667 - dense_1_acc_26: 0.4667 - dense_1_acc_27: 0.4500 - dense_1_acc_28: 0.5333 - dense_1_acc_29: 0.4833 - dense_1_acc_30: 0.0000e+00     
Epoch 19/100
60/60 [==============================] - 0s - loss: 61.2205 - dense_1_loss_1: 4.1340 - dense_1_loss_2: 3.6594 - dense_1_loss_3: 3.0283 - dense_1_loss_4: 2.7803 - dense_1_loss_5: 2.4655 - dense_1_loss_6: 2.2843 - dense_1_loss_7: 2.1789 - dense_1_loss_8: 1.9692 - dense_1_loss_9: 2.0161 - dense_1_loss_10: 2.0245 - dense_1_loss_11: 1.9559 - dense_1_loss_12: 1.8034 - dense_1_loss_13: 1.7186 - dense_1_loss_14: 1.6484 - dense_1_loss_15: 1.7976 - dense_1_loss_16: 1.8430 - dense_1_loss_17: 1.8413 - dense_1_loss_18: 1.7965 - dense_1_loss_19: 1.7226 - dense_1_loss_20: 1.8002 - dense_1_loss_21: 1.7837 - dense_1_loss_22: 1.8535 - dense_1_loss_23: 1.8136 - dense_1_loss_24: 1.9257 - dense_1_loss_25: 2.0409 - dense_1_loss_26: 1.7330 - dense_1_loss_27: 1.8764 - dense_1_loss_28: 1.8282 - dense_1_loss_29: 1.8973 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2000 - dense_1_acc_3: 0.3500 - dense_1_acc_4: 0.2667 - dense_1_acc_5: 0.3667 - dense_1_acc_6: 0.4833 - dense_1_acc_7: 0.4333 - dense_1_acc_8: 0.4167 - dense_1_acc_9: 0.4833 - dense_1_acc_10: 0.4333 - dense_1_acc_11: 0.4667 - dense_1_acc_12: 0.5500 - dense_1_acc_13: 0.5667 - dense_1_acc_14: 0.5500 - dense_1_acc_15: 0.4833 - dense_1_acc_16: 0.3500 - dense_1_acc_17: 0.4333 - dense_1_acc_18: 0.4333 - dense_1_acc_19: 0.5167 - dense_1_acc_20: 0.4167 - dense_1_acc_21: 0.4667 - dense_1_acc_22: 0.4000 - dense_1_acc_23: 0.5167 - dense_1_acc_24: 0.4000 - dense_1_acc_25: 0.2667 - dense_1_acc_26: 0.5167 - dense_1_acc_27: 0.3667 - dense_1_acc_28: 0.4833 - dense_1_acc_29: 0.5000 - dense_1_acc_30: 0.0000e+00     
Epoch 20/100
60/60 [==============================] - 0s - loss: 58.3665 - dense_1_loss_1: 4.1251 - dense_1_loss_2: 3.6124 - dense_1_loss_3: 2.9415 - dense_1_loss_4: 2.6814 - dense_1_loss_5: 2.3646 - dense_1_loss_6: 2.1645 - dense_1_loss_7: 2.0530 - dense_1_loss_8: 1.8901 - dense_1_loss_9: 1.9118 - dense_1_loss_10: 1.9114 - dense_1_loss_11: 1.8068 - dense_1_loss_12: 1.7091 - dense_1_loss_13: 1.6671 - dense_1_loss_14: 1.5997 - dense_1_loss_15: 1.6675 - dense_1_loss_16: 1.7883 - dense_1_loss_17: 1.7432 - dense_1_loss_18: 1.7086 - dense_1_loss_19: 1.6441 - dense_1_loss_20: 1.7361 - dense_1_loss_21: 1.6695 - dense_1_loss_22: 1.7681 - dense_1_loss_23: 1.6866 - dense_1_loss_24: 1.7884 - dense_1_loss_25: 1.8863 - dense_1_loss_26: 1.6428 - dense_1_loss_27: 1.7486 - dense_1_loss_28: 1.7017 - dense_1_loss_29: 1.7482 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0833 - dense_1_acc_2: 0.1833 - dense_1_acc_3: 0.3833 - dense_1_acc_4: 0.3000 - dense_1_acc_5: 0.3667 - dense_1_acc_6: 0.5000 - dense_1_acc_7: 0.4333 - dense_1_acc_8: 0.4333 - dense_1_acc_9: 0.5000 - dense_1_acc_10: 0.4667 - dense_1_acc_11: 0.5500 - dense_1_acc_12: 0.5167 - dense_1_acc_13: 0.6000 - dense_1_acc_14: 0.5667 - dense_1_acc_15: 0.5167 - dense_1_acc_16: 0.3667 - dense_1_acc_17: 0.4500 - dense_1_acc_18: 0.5167 - dense_1_acc_19: 0.5500 - dense_1_acc_20: 0.4167 - dense_1_acc_21: 0.5667 - dense_1_acc_22: 0.3667 - dense_1_acc_23: 0.5833 - dense_1_acc_24: 0.4667 - dense_1_acc_25: 0.3833 - dense_1_acc_26: 0.5667 - dense_1_acc_27: 0.5167 - dense_1_acc_28: 0.5833 - dense_1_acc_29: 0.6000 - dense_1_acc_30: 0.0000e+00     
Epoch 21/100
60/60 [==============================] - 0s - loss: 55.7536 - dense_1_loss_1: 4.1164 - dense_1_loss_2: 3.5676 - dense_1_loss_3: 2.8570 - dense_1_loss_4: 2.5783 - dense_1_loss_5: 2.2787 - dense_1_loss_6: 2.0611 - dense_1_loss_7: 1.9602 - dense_1_loss_8: 1.7737 - dense_1_loss_9: 1.8345 - dense_1_loss_10: 1.8497 - dense_1_loss_11: 1.7459 - dense_1_loss_12: 1.6051 - dense_1_loss_13: 1.5539 - dense_1_loss_14: 1.5096 - dense_1_loss_15: 1.6049 - dense_1_loss_16: 1.7314 - dense_1_loss_17: 1.6759 - dense_1_loss_18: 1.6068 - dense_1_loss_19: 1.5360 - dense_1_loss_20: 1.6365 - dense_1_loss_21: 1.5542 - dense_1_loss_22: 1.6501 - dense_1_loss_23: 1.6273 - dense_1_loss_24: 1.7119 - dense_1_loss_25: 1.7410 - dense_1_loss_26: 1.5141 - dense_1_loss_27: 1.5749 - dense_1_loss_28: 1.6396 - dense_1_loss_29: 1.6575 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2000 - dense_1_acc_3: 0.3833 - dense_1_acc_4: 0.3000 - dense_1_acc_5: 0.3500 - dense_1_acc_6: 0.5000 - dense_1_acc_7: 0.4833 - dense_1_acc_8: 0.4667 - dense_1_acc_9: 0.5500 - dense_1_acc_10: 0.4833 - dense_1_acc_11: 0.6000 - dense_1_acc_12: 0.5667 - dense_1_acc_13: 0.6000 - dense_1_acc_14: 0.6000 - dense_1_acc_15: 0.5000 - dense_1_acc_16: 0.5000 - dense_1_acc_17: 0.5833 - dense_1_acc_18: 0.6000 - dense_1_acc_19: 0.6167 - dense_1_acc_20: 0.6333 - dense_1_acc_21: 0.6500 - dense_1_acc_22: 0.5333 - dense_1_acc_23: 0.6333 - dense_1_acc_24: 0.4500 - dense_1_acc_25: 0.4667 - dense_1_acc_26: 0.6000 - dense_1_acc_27: 0.6000 - dense_1_acc_28: 0.5333 - dense_1_acc_29: 0.6500 - dense_1_acc_30: 0.0000e+00     
Epoch 22/100
60/60 [==============================] - 0s - loss: 53.0312 - dense_1_loss_1: 4.1088 - dense_1_loss_2: 3.5233 - dense_1_loss_3: 2.7698 - dense_1_loss_4: 2.4787 - dense_1_loss_5: 2.1921 - dense_1_loss_6: 1.9493 - dense_1_loss_7: 1.8396 - dense_1_loss_8: 1.7034 - dense_1_loss_9: 1.7224 - dense_1_loss_10: 1.7209 - dense_1_loss_11: 1.5619 - dense_1_loss_12: 1.4718 - dense_1_loss_13: 1.4496 - dense_1_loss_14: 1.3737 - dense_1_loss_15: 1.4580 - dense_1_loss_16: 1.6312 - dense_1_loss_17: 1.5443 - dense_1_loss_18: 1.5652 - dense_1_loss_19: 1.4747 - dense_1_loss_20: 1.4864 - dense_1_loss_21: 1.5145 - dense_1_loss_22: 1.5491 - dense_1_loss_23: 1.5311 - dense_1_loss_24: 1.6575 - dense_1_loss_25: 1.6370 - dense_1_loss_26: 1.4290 - dense_1_loss_27: 1.5803 - dense_1_loss_28: 1.5575 - dense_1_loss_29: 1.5499 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1833 - dense_1_acc_3: 0.4167 - dense_1_acc_4: 0.3167 - dense_1_acc_5: 0.3500 - dense_1_acc_6: 0.5667 - dense_1_acc_7: 0.5333 - dense_1_acc_8: 0.5167 - dense_1_acc_9: 0.5667 - dense_1_acc_10: 0.5167 - dense_1_acc_11: 0.6000 - dense_1_acc_12: 0.5833 - dense_1_acc_13: 0.6333 - dense_1_acc_14: 0.7000 - dense_1_acc_15: 0.6833 - dense_1_acc_16: 0.5833 - dense_1_acc_17: 0.6500 - dense_1_acc_18: 0.6333 - dense_1_acc_19: 0.6500 - dense_1_acc_20: 0.6333 - dense_1_acc_21: 0.6333 - dense_1_acc_22: 0.6333 - dense_1_acc_23: 0.5667 - dense_1_acc_24: 0.4500 - dense_1_acc_25: 0.4333 - dense_1_acc_26: 0.6333 - dense_1_acc_27: 0.5167 - dense_1_acc_28: 0.5833 - dense_1_acc_29: 0.7333 - dense_1_acc_30: 0.0000e+00     
Epoch 23/100
60/60 [==============================] - 0s - loss: 50.4750 - dense_1_loss_1: 4.0996 - dense_1_loss_2: 3.4787 - dense_1_loss_3: 2.6834 - dense_1_loss_4: 2.3797 - dense_1_loss_5: 2.0974 - dense_1_loss_6: 1.8650 - dense_1_loss_7: 1.7452 - dense_1_loss_8: 1.6162 - dense_1_loss_9: 1.6601 - dense_1_loss_10: 1.6134 - dense_1_loss_11: 1.5289 - dense_1_loss_12: 1.4325 - dense_1_loss_13: 1.3413 - dense_1_loss_14: 1.2793 - dense_1_loss_15: 1.3938 - dense_1_loss_16: 1.5346 - dense_1_loss_17: 1.4430 - dense_1_loss_18: 1.4643 - dense_1_loss_19: 1.3681 - dense_1_loss_20: 1.4101 - dense_1_loss_21: 1.4452 - dense_1_loss_22: 1.4580 - dense_1_loss_23: 1.4145 - dense_1_loss_24: 1.5080 - dense_1_loss_25: 1.5119 - dense_1_loss_26: 1.3657 - dense_1_loss_27: 1.4367 - dense_1_loss_28: 1.4232 - dense_1_loss_29: 1.4771 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2000 - dense_1_acc_3: 0.4167 - dense_1_acc_4: 0.3000 - dense_1_acc_5: 0.3667 - dense_1_acc_6: 0.5667 - dense_1_acc_7: 0.5333 - dense_1_acc_8: 0.4667 - dense_1_acc_9: 0.5000 - dense_1_acc_10: 0.4667 - dense_1_acc_11: 0.6167 - dense_1_acc_12: 0.5500 - dense_1_acc_13: 0.5833 - dense_1_acc_14: 0.6500 - dense_1_acc_15: 0.6167 - dense_1_acc_16: 0.5333 - dense_1_acc_17: 0.6667 - dense_1_acc_18: 0.5833 - dense_1_acc_19: 0.6500 - dense_1_acc_20: 0.6333 - dense_1_acc_21: 0.6167 - dense_1_acc_22: 0.5833 - dense_1_acc_23: 0.6500 - dense_1_acc_24: 0.5500 - dense_1_acc_25: 0.5333 - dense_1_acc_26: 0.6500 - dense_1_acc_27: 0.5833 - dense_1_acc_28: 0.6833 - dense_1_acc_29: 0.7333 - dense_1_acc_30: 0.0000e+00     
Epoch 24/100
60/60 [==============================] - 0s - loss: 48.0844 - dense_1_loss_1: 4.0919 - dense_1_loss_2: 3.4355 - dense_1_loss_3: 2.5977 - dense_1_loss_4: 2.2877 - dense_1_loss_5: 2.0180 - dense_1_loss_6: 1.7639 - dense_1_loss_7: 1.6453 - dense_1_loss_8: 1.5135 - dense_1_loss_9: 1.5833 - dense_1_loss_10: 1.4975 - dense_1_loss_11: 1.4588 - dense_1_loss_12: 1.3323 - dense_1_loss_13: 1.1961 - dense_1_loss_14: 1.2271 - dense_1_loss_15: 1.3383 - dense_1_loss_16: 1.4146 - dense_1_loss_17: 1.3665 - dense_1_loss_18: 1.3718 - dense_1_loss_19: 1.2681 - dense_1_loss_20: 1.3207 - dense_1_loss_21: 1.3518 - dense_1_loss_22: 1.3953 - dense_1_loss_23: 1.3484 - dense_1_loss_24: 1.3998 - dense_1_loss_25: 1.4556 - dense_1_loss_26: 1.2928 - dense_1_loss_27: 1.3529 - dense_1_loss_28: 1.3481 - dense_1_loss_29: 1.4111 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2000 - dense_1_acc_3: 0.3833 - dense_1_acc_4: 0.3000 - dense_1_acc_5: 0.3833 - dense_1_acc_6: 0.5667 - dense_1_acc_7: 0.5500 - dense_1_acc_8: 0.5333 - dense_1_acc_9: 0.6000 - dense_1_acc_10: 0.5333 - dense_1_acc_11: 0.5833 - dense_1_acc_12: 0.6500 - dense_1_acc_13: 0.7000 - dense_1_acc_14: 0.6500 - dense_1_acc_15: 0.5667 - dense_1_acc_16: 0.6333 - dense_1_acc_17: 0.7167 - dense_1_acc_18: 0.6333 - dense_1_acc_19: 0.7000 - dense_1_acc_20: 0.6333 - dense_1_acc_21: 0.5000 - dense_1_acc_22: 0.5667 - dense_1_acc_23: 0.6333 - dense_1_acc_24: 0.5500 - dense_1_acc_25: 0.5667 - dense_1_acc_26: 0.6833 - dense_1_acc_27: 0.6833 - dense_1_acc_28: 0.7333 - dense_1_acc_29: 0.6667 - dense_1_acc_30: 0.0000e+00     
Epoch 25/100
60/60 [==============================] - 0s - loss: 45.6157 - dense_1_loss_1: 4.0838 - dense_1_loss_2: 3.3891 - dense_1_loss_3: 2.5161 - dense_1_loss_4: 2.2011 - dense_1_loss_5: 1.9372 - dense_1_loss_6: 1.6786 - dense_1_loss_7: 1.5090 - dense_1_loss_8: 1.4601 - dense_1_loss_9: 1.4747 - dense_1_loss_10: 1.3630 - dense_1_loss_11: 1.3430 - dense_1_loss_12: 1.2825 - dense_1_loss_13: 1.1446 - dense_1_loss_14: 1.1209 - dense_1_loss_15: 1.2316 - dense_1_loss_16: 1.3478 - dense_1_loss_17: 1.2510 - dense_1_loss_18: 1.2607 - dense_1_loss_19: 1.2170 - dense_1_loss_20: 1.2524 - dense_1_loss_21: 1.2827 - dense_1_loss_22: 1.2756 - dense_1_loss_23: 1.2164 - dense_1_loss_24: 1.3316 - dense_1_loss_25: 1.3379 - dense_1_loss_26: 1.2125 - dense_1_loss_27: 1.3344 - dense_1_loss_28: 1.2560 - dense_1_loss_29: 1.3044 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2000 - dense_1_acc_3: 0.4500 - dense_1_acc_4: 0.3500 - dense_1_acc_5: 0.4167 - dense_1_acc_6: 0.5667 - dense_1_acc_7: 0.6167 - dense_1_acc_8: 0.5833 - dense_1_acc_9: 0.7000 - dense_1_acc_10: 0.5833 - dense_1_acc_11: 0.6833 - dense_1_acc_12: 0.7167 - dense_1_acc_13: 0.7833 - dense_1_acc_14: 0.7667 - dense_1_acc_15: 0.6667 - dense_1_acc_16: 0.6333 - dense_1_acc_17: 0.7500 - dense_1_acc_18: 0.7000 - dense_1_acc_19: 0.7500 - dense_1_acc_20: 0.7000 - dense_1_acc_21: 0.6167 - dense_1_acc_22: 0.7333 - dense_1_acc_23: 0.7667 - dense_1_acc_24: 0.6167 - dense_1_acc_25: 0.6333 - dense_1_acc_26: 0.7333 - dense_1_acc_27: 0.6667 - dense_1_acc_28: 0.7667 - dense_1_acc_29: 0.7500 - dense_1_acc_30: 0.0000e+00         
Epoch 26/100
60/60 [==============================] - 0s - loss: 43.3501 - dense_1_loss_1: 4.0764 - dense_1_loss_2: 3.3426 - dense_1_loss_3: 2.4337 - dense_1_loss_4: 2.1084 - dense_1_loss_5: 1.8493 - dense_1_loss_6: 1.5922 - dense_1_loss_7: 1.3873 - dense_1_loss_8: 1.3803 - dense_1_loss_9: 1.3851 - dense_1_loss_10: 1.2883 - dense_1_loss_11: 1.2507 - dense_1_loss_12: 1.2042 - dense_1_loss_13: 1.0932 - dense_1_loss_14: 1.0374 - dense_1_loss_15: 1.1596 - dense_1_loss_16: 1.2780 - dense_1_loss_17: 1.1806 - dense_1_loss_18: 1.1744 - dense_1_loss_19: 1.1216 - dense_1_loss_20: 1.1877 - dense_1_loss_21: 1.1855 - dense_1_loss_22: 1.1664 - dense_1_loss_23: 1.1432 - dense_1_loss_24: 1.2757 - dense_1_loss_25: 1.2405 - dense_1_loss_26: 1.1731 - dense_1_loss_27: 1.2184 - dense_1_loss_28: 1.1792 - dense_1_loss_29: 1.2372 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2000 - dense_1_acc_3: 0.5000 - dense_1_acc_4: 0.3667 - dense_1_acc_5: 0.4333 - dense_1_acc_6: 0.6500 - dense_1_acc_7: 0.7333 - dense_1_acc_8: 0.6667 - dense_1_acc_9: 0.7333 - dense_1_acc_10: 0.6500 - dense_1_acc_11: 0.6833 - dense_1_acc_12: 0.7333 - dense_1_acc_13: 0.8500 - dense_1_acc_14: 0.8500 - dense_1_acc_15: 0.7500 - dense_1_acc_16: 0.6667 - dense_1_acc_17: 0.7667 - dense_1_acc_18: 0.7667 - dense_1_acc_19: 0.8333 - dense_1_acc_20: 0.7833 - dense_1_acc_21: 0.7167 - dense_1_acc_22: 0.8167 - dense_1_acc_23: 0.8000 - dense_1_acc_24: 0.6833 - dense_1_acc_25: 0.6500 - dense_1_acc_26: 0.8333 - dense_1_acc_27: 0.7500 - dense_1_acc_28: 0.8000 - dense_1_acc_29: 0.7833 - dense_1_acc_30: 0.0000e+00     
Epoch 27/100
60/60 [==============================] - 0s - loss: 41.1283 - dense_1_loss_1: 4.0673 - dense_1_loss_2: 3.2960 - dense_1_loss_3: 2.3536 - dense_1_loss_4: 2.0112 - dense_1_loss_5: 1.7686 - dense_1_loss_6: 1.5016 - dense_1_loss_7: 1.2827 - dense_1_loss_8: 1.3025 - dense_1_loss_9: 1.2870 - dense_1_loss_10: 1.1844 - dense_1_loss_11: 1.1744 - dense_1_loss_12: 1.1058 - dense_1_loss_13: 1.0061 - dense_1_loss_14: 0.9815 - dense_1_loss_15: 1.0643 - dense_1_loss_16: 1.2345 - dense_1_loss_17: 1.1164 - dense_1_loss_18: 1.0789 - dense_1_loss_19: 1.0454 - dense_1_loss_20: 1.1163 - dense_1_loss_21: 1.1170 - dense_1_loss_22: 1.0579 - dense_1_loss_23: 1.1035 - dense_1_loss_24: 1.2045 - dense_1_loss_25: 1.1537 - dense_1_loss_26: 1.0949 - dense_1_loss_27: 1.1161 - dense_1_loss_28: 1.1357 - dense_1_loss_29: 1.1663 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2000 - dense_1_acc_3: 0.4833 - dense_1_acc_4: 0.4333 - dense_1_acc_5: 0.4500 - dense_1_acc_6: 0.6500 - dense_1_acc_7: 0.8333 - dense_1_acc_8: 0.6500 - dense_1_acc_9: 0.7333 - dense_1_acc_10: 0.7333 - dense_1_acc_11: 0.7833 - dense_1_acc_12: 0.8167 - dense_1_acc_13: 0.8500 - dense_1_acc_14: 0.9000 - dense_1_acc_15: 0.7833 - dense_1_acc_16: 0.7500 - dense_1_acc_17: 0.8500 - dense_1_acc_18: 0.8333 - dense_1_acc_19: 0.8833 - dense_1_acc_20: 0.8500 - dense_1_acc_21: 0.8333 - dense_1_acc_22: 0.8667 - dense_1_acc_23: 0.8167 - dense_1_acc_24: 0.7500 - dense_1_acc_25: 0.7333 - dense_1_acc_26: 0.8333 - dense_1_acc_27: 0.8333 - dense_1_acc_28: 0.8500 - dense_1_acc_29: 0.8500 - dense_1_acc_30: 0.0000e+00     
Epoch 28/100
60/60 [==============================] - 0s - loss: 39.0940 - dense_1_loss_1: 4.0586 - dense_1_loss_2: 3.2463 - dense_1_loss_3: 2.2749 - dense_1_loss_4: 1.9256 - dense_1_loss_5: 1.6915 - dense_1_loss_6: 1.4176 - dense_1_loss_7: 1.2084 - dense_1_loss_8: 1.2539 - dense_1_loss_9: 1.1880 - dense_1_loss_10: 1.1003 - dense_1_loss_11: 1.1228 - dense_1_loss_12: 1.0246 - dense_1_loss_13: 0.9372 - dense_1_loss_14: 0.9290 - dense_1_loss_15: 0.9897 - dense_1_loss_16: 1.1476 - dense_1_loss_17: 1.0277 - dense_1_loss_18: 1.0053 - dense_1_loss_19: 0.9961 - dense_1_loss_20: 1.0436 - dense_1_loss_21: 1.0558 - dense_1_loss_22: 1.0016 - dense_1_loss_23: 1.0465 - dense_1_loss_24: 1.1094 - dense_1_loss_25: 1.0769 - dense_1_loss_26: 1.0304 - dense_1_loss_27: 1.0544 - dense_1_loss_28: 1.0406 - dense_1_loss_29: 1.0901 - 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: 0.4500 - dense_1_acc_5: 0.4667 - dense_1_acc_6: 0.7000 - dense_1_acc_7: 0.8167 - dense_1_acc_8: 0.6833 - dense_1_acc_9: 0.7667 - dense_1_acc_10: 0.7500 - dense_1_acc_11: 0.8000 - dense_1_acc_12: 0.9167 - dense_1_acc_13: 0.9000 - dense_1_acc_14: 0.9000 - dense_1_acc_15: 0.8833 - dense_1_acc_16: 0.8000 - dense_1_acc_17: 0.9000 - dense_1_acc_18: 0.9667 - dense_1_acc_19: 0.9000 - dense_1_acc_20: 0.9000 - dense_1_acc_21: 0.8833 - dense_1_acc_22: 0.8833 - dense_1_acc_23: 0.8667 - dense_1_acc_24: 0.7667 - dense_1_acc_25: 0.7333 - dense_1_acc_26: 0.8667 - dense_1_acc_27: 0.8000 - dense_1_acc_28: 0.8333 - dense_1_acc_29: 0.8167 - dense_1_acc_30: 0.0000e+00     
Epoch 29/100
60/60 [==============================] - 0s - loss: 37.0770 - dense_1_loss_1: 4.0500 - dense_1_loss_2: 3.1993 - dense_1_loss_3: 2.2053 - dense_1_loss_4: 1.8378 - dense_1_loss_5: 1.5982 - dense_1_loss_6: 1.3360 - dense_1_loss_7: 1.1347 - dense_1_loss_8: 1.1784 - dense_1_loss_9: 1.1048 - dense_1_loss_10: 1.0159 - dense_1_loss_11: 1.0644 - dense_1_loss_12: 0.9660 - dense_1_loss_13: 0.8711 - dense_1_loss_14: 0.8419 - dense_1_loss_15: 0.9341 - dense_1_loss_16: 1.0316 - dense_1_loss_17: 0.9500 - dense_1_loss_18: 0.9345 - dense_1_loss_19: 0.9486 - dense_1_loss_20: 0.9849 - dense_1_loss_21: 0.9781 - dense_1_loss_22: 0.9633 - dense_1_loss_23: 0.9672 - dense_1_loss_24: 1.0326 - dense_1_loss_25: 1.0146 - dense_1_loss_26: 0.9510 - dense_1_loss_27: 0.9987 - dense_1_loss_28: 0.9585 - dense_1_loss_29: 1.0256 - 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: 0.4500 - dense_1_acc_5: 0.5000 - dense_1_acc_6: 0.6833 - dense_1_acc_7: 0.8167 - dense_1_acc_8: 0.7167 - dense_1_acc_9: 0.8333 - dense_1_acc_10: 0.8000 - dense_1_acc_11: 0.8500 - dense_1_acc_12: 0.9167 - dense_1_acc_13: 0.9167 - dense_1_acc_14: 0.9667 - dense_1_acc_15: 0.9000 - dense_1_acc_16: 0.8167 - dense_1_acc_17: 0.9333 - dense_1_acc_18: 0.9333 - dense_1_acc_19: 0.9167 - dense_1_acc_20: 0.9000 - dense_1_acc_21: 0.9167 - dense_1_acc_22: 0.8833 - dense_1_acc_23: 0.9167 - dense_1_acc_24: 0.8000 - dense_1_acc_25: 0.7833 - dense_1_acc_26: 0.9000 - dense_1_acc_27: 0.8667 - dense_1_acc_28: 0.8833 - dense_1_acc_29: 0.8667 - dense_1_acc_30: 0.0000e+00     
Epoch 30/100
60/60 [==============================] - 0s - loss: 35.1970 - dense_1_loss_1: 4.0419 - dense_1_loss_2: 3.1504 - dense_1_loss_3: 2.1342 - dense_1_loss_4: 1.7570 - dense_1_loss_5: 1.5156 - dense_1_loss_6: 1.2571 - dense_1_loss_7: 1.0462 - dense_1_loss_8: 1.1166 - dense_1_loss_9: 1.0240 - dense_1_loss_10: 0.9364 - dense_1_loss_11: 0.9673 - dense_1_loss_12: 0.8802 - dense_1_loss_13: 0.7993 - dense_1_loss_14: 0.7669 - dense_1_loss_15: 0.9054 - dense_1_loss_16: 0.9724 - dense_1_loss_17: 0.9205 - dense_1_loss_18: 0.8400 - dense_1_loss_19: 0.8878 - dense_1_loss_20: 0.9181 - dense_1_loss_21: 0.9311 - dense_1_loss_22: 0.8838 - dense_1_loss_23: 0.8995 - dense_1_loss_24: 0.9747 - dense_1_loss_25: 0.9624 - dense_1_loss_26: 0.8860 - dense_1_loss_27: 0.9432 - dense_1_loss_28: 0.9029 - dense_1_loss_29: 0.9760 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.5833 - dense_1_acc_4: 0.4500 - dense_1_acc_5: 0.5167 - dense_1_acc_6: 0.7167 - dense_1_acc_7: 0.8833 - dense_1_acc_8: 0.7833 - dense_1_acc_9: 0.8333 - dense_1_acc_10: 0.8333 - dense_1_acc_11: 0.8167 - dense_1_acc_12: 0.9167 - dense_1_acc_13: 0.9333 - dense_1_acc_14: 0.9833 - dense_1_acc_15: 0.8667 - dense_1_acc_16: 0.8500 - dense_1_acc_17: 0.9500 - dense_1_acc_18: 0.9667 - dense_1_acc_19: 0.9667 - dense_1_acc_20: 0.9333 - dense_1_acc_21: 0.9333 - dense_1_acc_22: 0.9167 - dense_1_acc_23: 0.9500 - dense_1_acc_24: 0.8667 - dense_1_acc_25: 0.8000 - dense_1_acc_26: 0.9333 - dense_1_acc_27: 0.8667 - dense_1_acc_28: 0.8833 - dense_1_acc_29: 0.8667 - dense_1_acc_30: 0.0000e+00     
Epoch 31/100
60/60 [==============================] - 0s - loss: 33.3930 - dense_1_loss_1: 4.0332 - dense_1_loss_2: 3.1085 - dense_1_loss_3: 2.0676 - dense_1_loss_4: 1.6745 - dense_1_loss_5: 1.4335 - dense_1_loss_6: 1.1682 - dense_1_loss_7: 0.9907 - dense_1_loss_8: 1.0384 - dense_1_loss_9: 0.9786 - dense_1_loss_10: 0.8741 - dense_1_loss_11: 0.9502 - dense_1_loss_12: 0.8222 - dense_1_loss_13: 0.7468 - dense_1_loss_14: 0.7257 - dense_1_loss_15: 0.8547 - dense_1_loss_16: 0.9012 - dense_1_loss_17: 0.8332 - dense_1_loss_18: 0.8084 - dense_1_loss_19: 0.8181 - dense_1_loss_20: 0.8630 - dense_1_loss_21: 0.8432 - dense_1_loss_22: 0.8325 - dense_1_loss_23: 0.8460 - dense_1_loss_24: 0.8546 - dense_1_loss_25: 0.8909 - dense_1_loss_26: 0.8243 - dense_1_loss_27: 0.8770 - dense_1_loss_28: 0.8452 - dense_1_loss_29: 0.8886 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.6000 - dense_1_acc_4: 0.4500 - dense_1_acc_5: 0.6000 - dense_1_acc_6: 0.7833 - dense_1_acc_7: 0.8167 - dense_1_acc_8: 0.8000 - dense_1_acc_9: 0.8667 - dense_1_acc_10: 0.8500 - dense_1_acc_11: 0.8500 - dense_1_acc_12: 0.9333 - dense_1_acc_13: 0.9833 - dense_1_acc_14: 0.9833 - dense_1_acc_15: 0.8667 - dense_1_acc_16: 0.9000 - dense_1_acc_17: 0.9500 - dense_1_acc_18: 0.9667 - dense_1_acc_19: 0.9667 - dense_1_acc_20: 0.9500 - dense_1_acc_21: 0.9333 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 0.9500 - dense_1_acc_24: 0.9333 - dense_1_acc_25: 0.8500 - dense_1_acc_26: 0.9167 - dense_1_acc_27: 0.9167 - 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: 31.6641 - dense_1_loss_1: 4.0257 - dense_1_loss_2: 3.0618 - dense_1_loss_3: 1.9982 - dense_1_loss_4: 1.5976 - dense_1_loss_5: 1.3514 - dense_1_loss_6: 1.0985 - dense_1_loss_7: 0.9273 - dense_1_loss_8: 0.9508 - dense_1_loss_9: 0.9118 - dense_1_loss_10: 0.7921 - dense_1_loss_11: 0.8801 - dense_1_loss_12: 0.7615 - dense_1_loss_13: 0.6755 - dense_1_loss_14: 0.6752 - dense_1_loss_15: 0.7727 - dense_1_loss_16: 0.8478 - dense_1_loss_17: 0.7551 - dense_1_loss_18: 0.7617 - dense_1_loss_19: 0.7565 - dense_1_loss_20: 0.7937 - dense_1_loss_21: 0.7962 - dense_1_loss_22: 0.7836 - dense_1_loss_23: 0.7903 - dense_1_loss_24: 0.8209 - dense_1_loss_25: 0.8426 - dense_1_loss_26: 0.7714 - dense_1_loss_27: 0.8276 - dense_1_loss_28: 0.8043 - dense_1_loss_29: 0.8323 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3333 - dense_1_acc_3: 0.6167 - dense_1_acc_4: 0.4667 - dense_1_acc_5: 0.6333 - dense_1_acc_6: 0.8167 - dense_1_acc_7: 0.8167 - dense_1_acc_8: 0.8167 - dense_1_acc_9: 0.8833 - dense_1_acc_10: 0.9333 - dense_1_acc_11: 0.8667 - dense_1_acc_12: 0.9500 - dense_1_acc_13: 0.9833 - dense_1_acc_14: 0.9833 - dense_1_acc_15: 0.9500 - dense_1_acc_16: 0.9167 - dense_1_acc_17: 0.9500 - dense_1_acc_18: 0.9667 - dense_1_acc_19: 0.9667 - dense_1_acc_20: 0.9667 - dense_1_acc_21: 0.9667 - dense_1_acc_22: 0.9500 - dense_1_acc_23: 0.9667 - dense_1_acc_24: 0.9500 - dense_1_acc_25: 0.8667 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 0.9500 - dense_1_acc_28: 0.9667 - dense_1_acc_29: 0.9000 - dense_1_acc_30: 0.0000e+00     
Epoch 33/100
60/60 [==============================] - 0s - loss: 29.9589 - dense_1_loss_1: 4.0187 - dense_1_loss_2: 3.0165 - dense_1_loss_3: 1.9323 - dense_1_loss_4: 1.5172 - dense_1_loss_5: 1.2831 - dense_1_loss_6: 1.0307 - dense_1_loss_7: 0.8652 - dense_1_loss_8: 0.8819 - dense_1_loss_9: 0.8349 - dense_1_loss_10: 0.7540 - dense_1_loss_11: 0.8088 - dense_1_loss_12: 0.6868 - dense_1_loss_13: 0.6365 - dense_1_loss_14: 0.6289 - dense_1_loss_15: 0.6985 - dense_1_loss_16: 0.7844 - dense_1_loss_17: 0.7064 - dense_1_loss_18: 0.6990 - dense_1_loss_19: 0.7020 - dense_1_loss_20: 0.7361 - dense_1_loss_21: 0.7612 - dense_1_loss_22: 0.7038 - dense_1_loss_23: 0.7280 - dense_1_loss_24: 0.7767 - dense_1_loss_25: 0.7684 - dense_1_loss_26: 0.7095 - dense_1_loss_27: 0.7637 - dense_1_loss_28: 0.7527 - dense_1_loss_29: 0.7730 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3333 - dense_1_acc_3: 0.6333 - dense_1_acc_4: 0.5333 - dense_1_acc_5: 0.6667 - dense_1_acc_6: 0.8333 - dense_1_acc_7: 0.8667 - dense_1_acc_8: 0.8167 - dense_1_acc_9: 0.8833 - dense_1_acc_10: 0.9333 - dense_1_acc_11: 0.8833 - dense_1_acc_12: 0.9500 - dense_1_acc_13: 0.9667 - dense_1_acc_14: 0.9833 - dense_1_acc_15: 0.9667 - dense_1_acc_16: 0.9000 - dense_1_acc_17: 0.9500 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9833 - dense_1_acc_20: 0.9667 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 0.9667 - dense_1_acc_24: 0.9167 - dense_1_acc_25: 0.8667 - dense_1_acc_26: 0.9667 - dense_1_acc_27: 0.9667 - dense_1_acc_28: 0.9500 - dense_1_acc_29: 0.9000 - dense_1_acc_30: 0.0000e+00     
Epoch 34/100
60/60 [==============================] - 0s - loss: 28.4402 - dense_1_loss_1: 4.0116 - dense_1_loss_2: 2.9734 - dense_1_loss_3: 1.8688 - dense_1_loss_4: 1.4311 - dense_1_loss_5: 1.2014 - dense_1_loss_6: 0.9620 - dense_1_loss_7: 0.8162 - dense_1_loss_8: 0.8130 - dense_1_loss_9: 0.7886 - dense_1_loss_10: 0.7295 - dense_1_loss_11: 0.7427 - dense_1_loss_12: 0.6425 - dense_1_loss_13: 0.6095 - dense_1_loss_14: 0.5956 - dense_1_loss_15: 0.6445 - dense_1_loss_16: 0.7192 - dense_1_loss_17: 0.6774 - dense_1_loss_18: 0.6331 - dense_1_loss_19: 0.6588 - dense_1_loss_20: 0.7049 - dense_1_loss_21: 0.7136 - dense_1_loss_22: 0.6409 - dense_1_loss_23: 0.6637 - dense_1_loss_24: 0.7207 - dense_1_loss_25: 0.6956 - dense_1_loss_26: 0.6724 - dense_1_loss_27: 0.6773 - dense_1_loss_28: 0.6936 - dense_1_loss_29: 0.7385 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3333 - dense_1_acc_3: 0.6333 - dense_1_acc_4: 0.6000 - dense_1_acc_5: 0.6667 - dense_1_acc_6: 0.8500 - dense_1_acc_7: 0.9333 - dense_1_acc_8: 0.8500 - dense_1_acc_9: 0.8833 - dense_1_acc_10: 0.9167 - dense_1_acc_11: 0.8833 - dense_1_acc_12: 0.9667 - dense_1_acc_13: 0.9500 - dense_1_acc_14: 0.9667 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 0.9333 - dense_1_acc_17: 0.9500 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9833 - dense_1_acc_20: 0.9500 - dense_1_acc_21: 0.9667 - dense_1_acc_22: 0.9833 - dense_1_acc_23: 0.9833 - dense_1_acc_24: 0.9333 - dense_1_acc_25: 0.9333 - dense_1_acc_26: 0.9667 - dense_1_acc_27: 0.9833 - dense_1_acc_28: 0.9333 - dense_1_acc_29: 0.9000 - dense_1_acc_30: 0.0000e+00     
Epoch 35/100
60/60 [==============================] - 0s - loss: 26.9189 - dense_1_loss_1: 4.0048 - dense_1_loss_2: 2.9288 - dense_1_loss_3: 1.8057 - dense_1_loss_4: 1.3541 - dense_1_loss_5: 1.1307 - dense_1_loss_6: 0.8966 - dense_1_loss_7: 0.7547 - dense_1_loss_8: 0.7586 - dense_1_loss_9: 0.7246 - dense_1_loss_10: 0.6575 - dense_1_loss_11: 0.6809 - dense_1_loss_12: 0.6001 - dense_1_loss_13: 0.5498 - dense_1_loss_14: 0.5385 - dense_1_loss_15: 0.5934 - dense_1_loss_16: 0.6692 - dense_1_loss_17: 0.6229 - dense_1_loss_18: 0.5908 - dense_1_loss_19: 0.5865 - dense_1_loss_20: 0.6483 - dense_1_loss_21: 0.6529 - dense_1_loss_22: 0.5960 - dense_1_loss_23: 0.6226 - dense_1_loss_24: 0.6828 - dense_1_loss_25: 0.6524 - dense_1_loss_26: 0.6209 - dense_1_loss_27: 0.6432 - dense_1_loss_28: 0.6577 - dense_1_loss_29: 0.6936 - 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: 0.6333 - dense_1_acc_5: 0.7000 - dense_1_acc_6: 0.8833 - dense_1_acc_7: 0.9500 - dense_1_acc_8: 0.8500 - dense_1_acc_9: 0.8833 - dense_1_acc_10: 0.9667 - dense_1_acc_11: 0.8833 - 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: 0.9500 - dense_1_acc_17: 0.9667 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9833 - dense_1_acc_20: 0.9667 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 0.9833 - dense_1_acc_24: 0.9500 - dense_1_acc_25: 0.9667 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - 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: 25.4703 - dense_1_loss_1: 3.9984 - dense_1_loss_2: 2.8848 - dense_1_loss_3: 1.7491 - dense_1_loss_4: 1.2817 - dense_1_loss_5: 1.0608 - dense_1_loss_6: 0.8313 - dense_1_loss_7: 0.7044 - dense_1_loss_8: 0.6929 - dense_1_loss_9: 0.6563 - dense_1_loss_10: 0.6023 - dense_1_loss_11: 0.6474 - dense_1_loss_12: 0.5623 - dense_1_loss_13: 0.4988 - dense_1_loss_14: 0.4945 - dense_1_loss_15: 0.5634 - dense_1_loss_16: 0.6007 - dense_1_loss_17: 0.5733 - dense_1_loss_18: 0.5412 - dense_1_loss_19: 0.5438 - dense_1_loss_20: 0.5881 - dense_1_loss_21: 0.6008 - dense_1_loss_22: 0.5613 - dense_1_loss_23: 0.5849 - dense_1_loss_24: 0.6155 - dense_1_loss_25: 0.6010 - dense_1_loss_26: 0.5757 - dense_1_loss_27: 0.6118 - dense_1_loss_28: 0.6212 - dense_1_loss_29: 0.6227 - 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: 0.7000 - dense_1_acc_5: 0.7000 - dense_1_acc_6: 0.8667 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 0.9000 - dense_1_acc_9: 0.9333 - dense_1_acc_10: 0.9833 - dense_1_acc_11: 0.9000 - dense_1_acc_12: 0.9667 - 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: 0.9833 - 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: 0.9833 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9667 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 37/100
60/60 [==============================] - 0s - loss: 24.2150 - dense_1_loss_1: 3.9918 - dense_1_loss_2: 2.8416 - dense_1_loss_3: 1.6927 - dense_1_loss_4: 1.2058 - dense_1_loss_5: 0.9972 - dense_1_loss_6: 0.7790 - dense_1_loss_7: 0.6655 - dense_1_loss_8: 0.6320 - dense_1_loss_9: 0.6143 - dense_1_loss_10: 0.5610 - dense_1_loss_11: 0.6159 - dense_1_loss_12: 0.5137 - dense_1_loss_13: 0.4625 - dense_1_loss_14: 0.4597 - dense_1_loss_15: 0.5295 - dense_1_loss_16: 0.5394 - dense_1_loss_17: 0.5299 - dense_1_loss_18: 0.4959 - dense_1_loss_19: 0.5137 - dense_1_loss_20: 0.5521 - dense_1_loss_21: 0.5716 - dense_1_loss_22: 0.5196 - dense_1_loss_23: 0.5409 - dense_1_loss_24: 0.5520 - dense_1_loss_25: 0.5520 - dense_1_loss_26: 0.5507 - dense_1_loss_27: 0.5850 - dense_1_loss_28: 0.5873 - dense_1_loss_29: 0.5627 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3500 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.7500 - dense_1_acc_5: 0.7333 - dense_1_acc_6: 0.9000 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 0.9667 - dense_1_acc_9: 0.9167 - dense_1_acc_10: 0.9833 - dense_1_acc_11: 0.9000 - dense_1_acc_12: 0.9667 - 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: 0.9833 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 0.9833 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9500 - dense_1_acc_30: 0.0000e+00     
Epoch 38/100
60/60 [==============================] - 0s - loss: 22.8966 - dense_1_loss_1: 3.9860 - dense_1_loss_2: 2.8015 - dense_1_loss_3: 1.6380 - dense_1_loss_4: 1.1409 - dense_1_loss_5: 0.9299 - dense_1_loss_6: 0.7318 - dense_1_loss_7: 0.6078 - dense_1_loss_8: 0.5843 - dense_1_loss_9: 0.5629 - dense_1_loss_10: 0.5136 - dense_1_loss_11: 0.5556 - dense_1_loss_12: 0.4712 - dense_1_loss_13: 0.4189 - dense_1_loss_14: 0.4234 - dense_1_loss_15: 0.4795 - dense_1_loss_16: 0.5186 - dense_1_loss_17: 0.4793 - dense_1_loss_18: 0.4593 - dense_1_loss_19: 0.4614 - dense_1_loss_20: 0.5063 - dense_1_loss_21: 0.5371 - dense_1_loss_22: 0.4804 - dense_1_loss_23: 0.4858 - dense_1_loss_24: 0.5303 - dense_1_loss_25: 0.5314 - dense_1_loss_26: 0.4983 - dense_1_loss_27: 0.5117 - dense_1_loss_28: 0.5289 - dense_1_loss_29: 0.5223 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3500 - dense_1_acc_3: 0.6833 - dense_1_acc_4: 0.7667 - dense_1_acc_5: 0.7333 - dense_1_acc_6: 0.9167 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9500 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9000 - dense_1_acc_12: 0.9667 - 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: 0.9833 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - 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 39/100
60/60 [==============================] - 0s - loss: 21.7646 - dense_1_loss_1: 3.9807 - dense_1_loss_2: 2.7591 - dense_1_loss_3: 1.5838 - dense_1_loss_4: 1.0754 - dense_1_loss_5: 0.8699 - dense_1_loss_6: 0.6812 - dense_1_loss_7: 0.5732 - dense_1_loss_8: 0.5350 - dense_1_loss_9: 0.5253 - dense_1_loss_10: 0.4848 - dense_1_loss_11: 0.5100 - dense_1_loss_12: 0.4362 - dense_1_loss_13: 0.3945 - dense_1_loss_14: 0.3911 - dense_1_loss_15: 0.4466 - dense_1_loss_16: 0.4753 - dense_1_loss_17: 0.4518 - dense_1_loss_18: 0.4297 - dense_1_loss_19: 0.4294 - dense_1_loss_20: 0.4670 - dense_1_loss_21: 0.4957 - dense_1_loss_22: 0.4384 - dense_1_loss_23: 0.4465 - dense_1_loss_24: 0.4973 - dense_1_loss_25: 0.4719 - dense_1_loss_26: 0.4621 - dense_1_loss_27: 0.4699 - dense_1_loss_28: 0.4883 - dense_1_loss_29: 0.4946 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3500 - dense_1_acc_3: 0.6833 - dense_1_acc_4: 0.7833 - dense_1_acc_5: 0.7500 - dense_1_acc_6: 0.9333 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9500 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9667 - 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: 0.9667 - dense_1_acc_25: 0.9833 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9667 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 40/100
60/60 [==============================] - 0s - loss: 20.6540 - dense_1_loss_1: 3.9750 - dense_1_loss_2: 2.7191 - dense_1_loss_3: 1.5334 - dense_1_loss_4: 1.0056 - dense_1_loss_5: 0.8185 - dense_1_loss_6: 0.6229 - dense_1_loss_7: 0.5402 - dense_1_loss_8: 0.4812 - dense_1_loss_9: 0.4871 - dense_1_loss_10: 0.4444 - dense_1_loss_11: 0.4835 - dense_1_loss_12: 0.4012 - dense_1_loss_13: 0.3707 - dense_1_loss_14: 0.3617 - dense_1_loss_15: 0.4118 - dense_1_loss_16: 0.4345 - dense_1_loss_17: 0.4035 - dense_1_loss_18: 0.3930 - dense_1_loss_19: 0.4026 - dense_1_loss_20: 0.4249 - dense_1_loss_21: 0.4511 - dense_1_loss_22: 0.4104 - dense_1_loss_23: 0.4105 - dense_1_loss_24: 0.4402 - dense_1_loss_25: 0.4456 - dense_1_loss_26: 0.4280 - dense_1_loss_27: 0.4453 - dense_1_loss_28: 0.4597 - dense_1_loss_29: 0.4486 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3500 - dense_1_acc_3: 0.6833 - dense_1_acc_4: 0.8667 - dense_1_acc_5: 0.7667 - dense_1_acc_6: 0.9333 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9500 - 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: 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: 0.9833 - 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: 0.9833 - dense_1_acc_29: 0.9500 - dense_1_acc_30: 0.0000e+00     
Epoch 41/100
60/60 [==============================] - 0s - loss: 19.6047 - dense_1_loss_1: 3.9698 - dense_1_loss_2: 2.6774 - dense_1_loss_3: 1.4834 - dense_1_loss_4: 0.9434 - dense_1_loss_5: 0.7680 - dense_1_loss_6: 0.5788 - dense_1_loss_7: 0.4948 - dense_1_loss_8: 0.4484 - dense_1_loss_9: 0.4427 - dense_1_loss_10: 0.4110 - dense_1_loss_11: 0.4387 - dense_1_loss_12: 0.3622 - dense_1_loss_13: 0.3366 - dense_1_loss_14: 0.3308 - dense_1_loss_15: 0.3776 - dense_1_loss_16: 0.3968 - dense_1_loss_17: 0.3685 - dense_1_loss_18: 0.3650 - dense_1_loss_19: 0.3637 - dense_1_loss_20: 0.3923 - dense_1_loss_21: 0.4245 - dense_1_loss_22: 0.3733 - dense_1_loss_23: 0.3816 - dense_1_loss_24: 0.4064 - dense_1_loss_25: 0.4115 - dense_1_loss_26: 0.4000 - dense_1_loss_27: 0.4141 - dense_1_loss_28: 0.4249 - dense_1_loss_29: 0.4185 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3500 - dense_1_acc_3: 0.7000 - dense_1_acc_4: 0.8667 - dense_1_acc_5: 0.8333 - dense_1_acc_6: 0.9333 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9500 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9667 - 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: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 42/100
60/60 [==============================] - 0s - loss: 18.7135 - dense_1_loss_1: 3.9639 - dense_1_loss_2: 2.6392 - dense_1_loss_3: 1.4349 - dense_1_loss_4: 0.8890 - dense_1_loss_5: 0.7217 - dense_1_loss_6: 0.5429 - dense_1_loss_7: 0.4572 - dense_1_loss_8: 0.4214 - dense_1_loss_9: 0.4133 - dense_1_loss_10: 0.3806 - dense_1_loss_11: 0.3999 - dense_1_loss_12: 0.3382 - dense_1_loss_13: 0.3081 - dense_1_loss_14: 0.3085 - dense_1_loss_15: 0.3556 - dense_1_loss_16: 0.3598 - dense_1_loss_17: 0.3531 - dense_1_loss_18: 0.3415 - dense_1_loss_19: 0.3402 - dense_1_loss_20: 0.3786 - dense_1_loss_21: 0.3960 - dense_1_loss_22: 0.3338 - dense_1_loss_23: 0.3558 - dense_1_loss_24: 0.3831 - dense_1_loss_25: 0.3778 - dense_1_loss_26: 0.3623 - dense_1_loss_27: 0.3754 - dense_1_loss_28: 0.3908 - dense_1_loss_29: 0.3905 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3500 - dense_1_acc_3: 0.7000 - dense_1_acc_4: 0.8667 - dense_1_acc_5: 0.8333 - dense_1_acc_6: 0.9333 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9667 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9833 - 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: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00         
Epoch 43/100
60/60 [==============================] - 0s - loss: 17.7595 - dense_1_loss_1: 3.9589 - dense_1_loss_2: 2.5972 - dense_1_loss_3: 1.3873 - dense_1_loss_4: 0.8341 - dense_1_loss_5: 0.6752 - dense_1_loss_6: 0.5047 - dense_1_loss_7: 0.4228 - dense_1_loss_8: 0.3833 - dense_1_loss_9: 0.3817 - dense_1_loss_10: 0.3466 - dense_1_loss_11: 0.3661 - dense_1_loss_12: 0.3145 - dense_1_loss_13: 0.2805 - dense_1_loss_14: 0.2765 - dense_1_loss_15: 0.3285 - dense_1_loss_16: 0.3361 - dense_1_loss_17: 0.3263 - dense_1_loss_18: 0.3057 - dense_1_loss_19: 0.3002 - dense_1_loss_20: 0.3582 - dense_1_loss_21: 0.3506 - dense_1_loss_22: 0.3130 - dense_1_loss_23: 0.3292 - dense_1_loss_24: 0.3379 - dense_1_loss_25: 0.3544 - dense_1_loss_26: 0.3387 - dense_1_loss_27: 0.3441 - dense_1_loss_28: 0.3494 - dense_1_loss_29: 0.3579 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3667 - dense_1_acc_3: 0.7000 - dense_1_acc_4: 0.8667 - dense_1_acc_5: 0.8500 - dense_1_acc_6: 0.9500 - dense_1_acc_7: 0.9667 - 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: 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: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 44/100
60/60 [==============================] - 0s - loss: 17.0206 - dense_1_loss_1: 3.9533 - dense_1_loss_2: 2.5584 - dense_1_loss_3: 1.3444 - dense_1_loss_4: 0.7805 - dense_1_loss_5: 0.6362 - dense_1_loss_6: 0.4690 - dense_1_loss_7: 0.3947 - dense_1_loss_8: 0.3505 - dense_1_loss_9: 0.3558 - dense_1_loss_10: 0.3171 - dense_1_loss_11: 0.3394 - dense_1_loss_12: 0.2906 - dense_1_loss_13: 0.2610 - dense_1_loss_14: 0.2550 - dense_1_loss_15: 0.3009 - dense_1_loss_16: 0.3143 - dense_1_loss_17: 0.3016 - dense_1_loss_18: 0.2846 - dense_1_loss_19: 0.2811 - dense_1_loss_20: 0.3282 - dense_1_loss_21: 0.3327 - dense_1_loss_22: 0.3021 - dense_1_loss_23: 0.3067 - dense_1_loss_24: 0.3082 - dense_1_loss_25: 0.3403 - dense_1_loss_26: 0.3190 - dense_1_loss_27: 0.3268 - dense_1_loss_28: 0.3364 - dense_1_loss_29: 0.3317 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3500 - dense_1_acc_3: 0.7000 - dense_1_acc_4: 0.8833 - dense_1_acc_5: 0.8833 - dense_1_acc_6: 0.9500 - dense_1_acc_7: 0.9667 - 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: 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: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 45/100
60/60 [==============================] - 0s - loss: 16.2297 - dense_1_loss_1: 3.9490 - dense_1_loss_2: 2.5187 - dense_1_loss_3: 1.3012 - dense_1_loss_4: 0.7359 - dense_1_loss_5: 0.5967 - dense_1_loss_6: 0.4365 - dense_1_loss_7: 0.3622 - dense_1_loss_8: 0.3245 - dense_1_loss_9: 0.3255 - dense_1_loss_10: 0.2942 - dense_1_loss_11: 0.3095 - dense_1_loss_12: 0.2663 - dense_1_loss_13: 0.2359 - dense_1_loss_14: 0.2400 - dense_1_loss_15: 0.2724 - dense_1_loss_16: 0.2800 - dense_1_loss_17: 0.2832 - dense_1_loss_18: 0.2656 - dense_1_loss_19: 0.2570 - dense_1_loss_20: 0.3066 - dense_1_loss_21: 0.3125 - dense_1_loss_22: 0.2666 - dense_1_loss_23: 0.2855 - dense_1_loss_24: 0.2911 - dense_1_loss_25: 0.2896 - dense_1_loss_26: 0.2990 - dense_1_loss_27: 0.3051 - dense_1_loss_28: 0.3156 - dense_1_loss_29: 0.3039 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3667 - dense_1_acc_3: 0.7000 - dense_1_acc_4: 0.8833 - 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: 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: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 46/100
60/60 [==============================] - 0s - loss: 15.5385 - dense_1_loss_1: 3.9436 - dense_1_loss_2: 2.4799 - dense_1_loss_3: 1.2621 - dense_1_loss_4: 0.6928 - dense_1_loss_5: 0.5635 - dense_1_loss_6: 0.4107 - dense_1_loss_7: 0.3353 - dense_1_loss_8: 0.3042 - dense_1_loss_9: 0.3011 - dense_1_loss_10: 0.2746 - dense_1_loss_11: 0.2859 - dense_1_loss_12: 0.2475 - dense_1_loss_13: 0.2170 - dense_1_loss_14: 0.2305 - dense_1_loss_15: 0.2512 - dense_1_loss_16: 0.2615 - dense_1_loss_17: 0.2634 - dense_1_loss_18: 0.2501 - dense_1_loss_19: 0.2397 - dense_1_loss_20: 0.2752 - dense_1_loss_21: 0.2892 - dense_1_loss_22: 0.2469 - dense_1_loss_23: 0.2560 - dense_1_loss_24: 0.2750 - dense_1_loss_25: 0.2773 - dense_1_loss_26: 0.2611 - dense_1_loss_27: 0.2720 - dense_1_loss_28: 0.2899 - dense_1_loss_29: 0.2813 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.7000 - dense_1_acc_4: 0.9000 - dense_1_acc_5: 0.9000 - 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: 0.9833 - 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: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 47/100
60/60 [==============================] - 0s - loss: 14.8822 - dense_1_loss_1: 3.9386 - dense_1_loss_2: 2.4418 - dense_1_loss_3: 1.2249 - dense_1_loss_4: 0.6529 - dense_1_loss_5: 0.5259 - dense_1_loss_6: 0.3800 - dense_1_loss_7: 0.3158 - dense_1_loss_8: 0.2789 - dense_1_loss_9: 0.2818 - dense_1_loss_10: 0.2495 - dense_1_loss_11: 0.2645 - dense_1_loss_12: 0.2283 - dense_1_loss_13: 0.1987 - dense_1_loss_14: 0.2122 - dense_1_loss_15: 0.2325 - dense_1_loss_16: 0.2414 - dense_1_loss_17: 0.2360 - dense_1_loss_18: 0.2308 - dense_1_loss_19: 0.2230 - dense_1_loss_20: 0.2505 - dense_1_loss_21: 0.2609 - dense_1_loss_22: 0.2339 - dense_1_loss_23: 0.2378 - dense_1_loss_24: 0.2457 - dense_1_loss_25: 0.2679 - dense_1_loss_26: 0.2447 - dense_1_loss_27: 0.2503 - dense_1_loss_28: 0.2655 - dense_1_loss_29: 0.2674 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.7000 - dense_1_acc_4: 0.9000 - 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: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 48/100
60/60 [==============================] - 0s - loss: 14.2898 - dense_1_loss_1: 3.9339 - dense_1_loss_2: 2.4039 - dense_1_loss_3: 1.1900 - dense_1_loss_4: 0.6147 - dense_1_loss_5: 0.4957 - dense_1_loss_6: 0.3558 - dense_1_loss_7: 0.2955 - dense_1_loss_8: 0.2567 - dense_1_loss_9: 0.2632 - dense_1_loss_10: 0.2310 - dense_1_loss_11: 0.2458 - dense_1_loss_12: 0.2123 - dense_1_loss_13: 0.1878 - dense_1_loss_14: 0.1930 - dense_1_loss_15: 0.2190 - dense_1_loss_16: 0.2212 - dense_1_loss_17: 0.2175 - dense_1_loss_18: 0.2089 - dense_1_loss_19: 0.2081 - dense_1_loss_20: 0.2340 - dense_1_loss_21: 0.2425 - dense_1_loss_22: 0.2187 - dense_1_loss_23: 0.2276 - dense_1_loss_24: 0.2229 - dense_1_loss_25: 0.2340 - dense_1_loss_26: 0.2308 - dense_1_loss_27: 0.2416 - dense_1_loss_28: 0.2466 - dense_1_loss_29: 0.2372 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.7167 - dense_1_acc_4: 0.9167 - 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: 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: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 49/100
60/60 [==============================] - 0s - loss: 13.7533 - dense_1_loss_1: 3.9296 - dense_1_loss_2: 2.3675 - dense_1_loss_3: 1.1522 - dense_1_loss_4: 0.5800 - dense_1_loss_5: 0.4656 - dense_1_loss_6: 0.3341 - dense_1_loss_7: 0.2750 - dense_1_loss_8: 0.2414 - dense_1_loss_9: 0.2434 - dense_1_loss_10: 0.2138 - dense_1_loss_11: 0.2274 - dense_1_loss_12: 0.1976 - dense_1_loss_13: 0.1747 - dense_1_loss_14: 0.1785 - dense_1_loss_15: 0.2021 - dense_1_loss_16: 0.2051 - dense_1_loss_17: 0.2026 - dense_1_loss_18: 0.1957 - dense_1_loss_19: 0.1925 - dense_1_loss_20: 0.2193 - dense_1_loss_21: 0.2294 - dense_1_loss_22: 0.1999 - dense_1_loss_23: 0.2129 - dense_1_loss_24: 0.2047 - dense_1_loss_25: 0.2119 - dense_1_loss_26: 0.2180 - dense_1_loss_27: 0.2288 - dense_1_loss_28: 0.2333 - dense_1_loss_29: 0.2163 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.7333 - dense_1_acc_4: 0.9167 - 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: 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: 0.9833 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 50/100
60/60 [==============================] - 0s - loss: 13.2399 - dense_1_loss_1: 3.9251 - dense_1_loss_2: 2.3306 - dense_1_loss_3: 1.1152 - dense_1_loss_4: 0.5439 - dense_1_loss_5: 0.4373 - dense_1_loss_6: 0.3125 - dense_1_loss_7: 0.2526 - dense_1_loss_8: 0.2255 - dense_1_loss_9: 0.2238 - dense_1_loss_10: 0.1975 - dense_1_loss_11: 0.2063 - dense_1_loss_12: 0.1825 - dense_1_loss_13: 0.1615 - dense_1_loss_14: 0.1667 - dense_1_loss_15: 0.1877 - dense_1_loss_16: 0.1903 - dense_1_loss_17: 0.1926 - dense_1_loss_18: 0.1835 - dense_1_loss_19: 0.1768 - dense_1_loss_20: 0.2033 - dense_1_loss_21: 0.2125 - dense_1_loss_22: 0.1885 - dense_1_loss_23: 0.1945 - dense_1_loss_24: 0.1932 - dense_1_loss_25: 0.2013 - dense_1_loss_26: 0.1969 - dense_1_loss_27: 0.2111 - dense_1_loss_28: 0.2179 - dense_1_loss_29: 0.2087 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.7500 - dense_1_acc_4: 0.9167 - 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: 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: 0.9833 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 51/100
60/60 [==============================] - 0s - loss: 12.7763 - dense_1_loss_1: 3.9206 - dense_1_loss_2: 2.2942 - dense_1_loss_3: 1.0831 - dense_1_loss_4: 0.5154 - dense_1_loss_5: 0.4096 - dense_1_loss_6: 0.2914 - dense_1_loss_7: 0.2384 - dense_1_loss_8: 0.2071 - dense_1_loss_9: 0.2100 - dense_1_loss_10: 0.1861 - dense_1_loss_11: 0.1908 - dense_1_loss_12: 0.1723 - dense_1_loss_13: 0.1499 - dense_1_loss_14: 0.1563 - dense_1_loss_15: 0.1740 - dense_1_loss_16: 0.1737 - dense_1_loss_17: 0.1798 - dense_1_loss_18: 0.1702 - dense_1_loss_19: 0.1638 - dense_1_loss_20: 0.1911 - dense_1_loss_21: 0.1968 - dense_1_loss_22: 0.1745 - dense_1_loss_23: 0.1770 - dense_1_loss_24: 0.1811 - dense_1_loss_25: 0.1909 - dense_1_loss_26: 0.1833 - dense_1_loss_27: 0.1907 - dense_1_loss_28: 0.2044 - dense_1_loss_29: 0.2000 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.7500 - dense_1_acc_4: 0.9167 - 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: 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: 0.9833 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 52/100
60/60 [==============================] - 0s - loss: 12.3452 - dense_1_loss_1: 3.9166 - dense_1_loss_2: 2.2604 - dense_1_loss_3: 1.0519 - dense_1_loss_4: 0.4888 - dense_1_loss_5: 0.3876 - dense_1_loss_6: 0.2748 - dense_1_loss_7: 0.2242 - dense_1_loss_8: 0.1938 - dense_1_loss_9: 0.1944 - dense_1_loss_10: 0.1765 - dense_1_loss_11: 0.1766 - dense_1_loss_12: 0.1606 - dense_1_loss_13: 0.1411 - dense_1_loss_14: 0.1444 - dense_1_loss_15: 0.1638 - dense_1_loss_16: 0.1610 - dense_1_loss_17: 0.1670 - dense_1_loss_18: 0.1568 - dense_1_loss_19: 0.1520 - dense_1_loss_20: 0.1801 - dense_1_loss_21: 0.1859 - dense_1_loss_22: 0.1580 - dense_1_loss_23: 0.1651 - dense_1_loss_24: 0.1681 - dense_1_loss_25: 0.1761 - dense_1_loss_26: 0.1703 - dense_1_loss_27: 0.1805 - dense_1_loss_28: 0.1905 - dense_1_loss_29: 0.1786 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4000 - dense_1_acc_3: 0.7667 - 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: 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: 0.9833 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 53/100
60/60 [==============================] - 0s - loss: 11.9593 - dense_1_loss_1: 3.9120 - dense_1_loss_2: 2.2261 - dense_1_loss_3: 1.0206 - dense_1_loss_4: 0.4645 - dense_1_loss_5: 0.3653 - dense_1_loss_6: 0.2575 - dense_1_loss_7: 0.2112 - dense_1_loss_8: 0.1816 - dense_1_loss_9: 0.1817 - dense_1_loss_10: 0.1625 - dense_1_loss_11: 0.1664 - dense_1_loss_12: 0.1488 - dense_1_loss_13: 0.1309 - dense_1_loss_14: 0.1331 - dense_1_loss_15: 0.1534 - dense_1_loss_16: 0.1516 - dense_1_loss_17: 0.1548 - dense_1_loss_18: 0.1476 - dense_1_loss_19: 0.1423 - dense_1_loss_20: 0.1659 - dense_1_loss_21: 0.1737 - dense_1_loss_22: 0.1506 - dense_1_loss_23: 0.1566 - dense_1_loss_24: 0.1570 - dense_1_loss_25: 0.1635 - dense_1_loss_26: 0.1585 - dense_1_loss_27: 0.1763 - dense_1_loss_28: 0.1803 - dense_1_loss_29: 0.1647 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4000 - dense_1_acc_3: 0.7667 - 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: 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: 0.9833 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 54/100
60/60 [==============================] - 0s - loss: 11.5978 - dense_1_loss_1: 3.9082 - dense_1_loss_2: 2.1933 - dense_1_loss_3: 0.9907 - dense_1_loss_4: 0.4402 - dense_1_loss_5: 0.3433 - dense_1_loss_6: 0.2428 - dense_1_loss_7: 0.1977 - dense_1_loss_8: 0.1713 - dense_1_loss_9: 0.1695 - dense_1_loss_10: 0.1521 - dense_1_loss_11: 0.1542 - dense_1_loss_12: 0.1394 - dense_1_loss_13: 0.1221 - dense_1_loss_14: 0.1245 - dense_1_loss_15: 0.1433 - dense_1_loss_16: 0.1429 - dense_1_loss_17: 0.1446 - dense_1_loss_18: 0.1389 - dense_1_loss_19: 0.1343 - dense_1_loss_20: 0.1548 - dense_1_loss_21: 0.1590 - dense_1_loss_22: 0.1452 - dense_1_loss_23: 0.1462 - dense_1_loss_24: 0.1452 - dense_1_loss_25: 0.1513 - dense_1_loss_26: 0.1486 - dense_1_loss_27: 0.1695 - dense_1_loss_28: 0.1697 - dense_1_loss_29: 0.1552 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4333 - dense_1_acc_3: 0.7667 - 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: 0.9833 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 55/100
60/60 [==============================] - 0s - loss: 11.2618 - dense_1_loss_1: 3.9040 - dense_1_loss_2: 2.1591 - dense_1_loss_3: 0.9625 - dense_1_loss_4: 0.4180 - dense_1_loss_5: 0.3245 - dense_1_loss_6: 0.2290 - dense_1_loss_7: 0.1858 - dense_1_loss_8: 0.1612 - dense_1_loss_9: 0.1586 - dense_1_loss_10: 0.1431 - dense_1_loss_11: 0.1437 - dense_1_loss_12: 0.1322 - dense_1_loss_13: 0.1153 - dense_1_loss_14: 0.1192 - dense_1_loss_15: 0.1319 - dense_1_loss_16: 0.1334 - dense_1_loss_17: 0.1361 - dense_1_loss_18: 0.1295 - dense_1_loss_19: 0.1278 - dense_1_loss_20: 0.1453 - dense_1_loss_21: 0.1505 - dense_1_loss_22: 0.1343 - dense_1_loss_23: 0.1360 - dense_1_loss_24: 0.1376 - dense_1_loss_25: 0.1419 - dense_1_loss_26: 0.1399 - dense_1_loss_27: 0.1547 - dense_1_loss_28: 0.1590 - dense_1_loss_29: 0.1477 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5000 - dense_1_acc_3: 0.7833 - dense_1_acc_4: 0.9500 - 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 56/100
60/60 [==============================] - 0s - loss: 10.9514 - dense_1_loss_1: 3.9003 - dense_1_loss_2: 2.1289 - dense_1_loss_3: 0.9355 - dense_1_loss_4: 0.3978 - dense_1_loss_5: 0.3042 - dense_1_loss_6: 0.2156 - dense_1_loss_7: 0.1734 - dense_1_loss_8: 0.1520 - dense_1_loss_9: 0.1481 - dense_1_loss_10: 0.1348 - dense_1_loss_11: 0.1336 - dense_1_loss_12: 0.1247 - dense_1_loss_13: 0.1091 - dense_1_loss_14: 0.1136 - dense_1_loss_15: 0.1235 - dense_1_loss_16: 0.1246 - dense_1_loss_17: 0.1283 - dense_1_loss_18: 0.1217 - dense_1_loss_19: 0.1183 - dense_1_loss_20: 0.1371 - dense_1_loss_21: 0.1455 - dense_1_loss_22: 0.1223 - dense_1_loss_23: 0.1284 - dense_1_loss_24: 0.1300 - dense_1_loss_25: 0.1344 - dense_1_loss_26: 0.1319 - dense_1_loss_27: 0.1425 - dense_1_loss_28: 0.1520 - dense_1_loss_29: 0.1394 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5000 - dense_1_acc_3: 0.7833 - dense_1_acc_4: 0.9500 - 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 57/100
60/60 [==============================] - 0s - loss: 10.6661 - dense_1_loss_1: 3.8963 - dense_1_loss_2: 2.0969 - dense_1_loss_3: 0.9104 - dense_1_loss_4: 0.3793 - dense_1_loss_5: 0.2898 - dense_1_loss_6: 0.2033 - dense_1_loss_7: 0.1646 - dense_1_loss_8: 0.1427 - dense_1_loss_9: 0.1398 - dense_1_loss_10: 0.1253 - dense_1_loss_11: 0.1267 - dense_1_loss_12: 0.1176 - dense_1_loss_13: 0.1024 - dense_1_loss_14: 0.1057 - dense_1_loss_15: 0.1177 - dense_1_loss_16: 0.1159 - dense_1_loss_17: 0.1209 - dense_1_loss_18: 0.1140 - dense_1_loss_19: 0.1099 - dense_1_loss_20: 0.1287 - dense_1_loss_21: 0.1377 - dense_1_loss_22: 0.1143 - dense_1_loss_23: 0.1220 - dense_1_loss_24: 0.1221 - dense_1_loss_25: 0.1272 - dense_1_loss_26: 0.1245 - dense_1_loss_27: 0.1364 - dense_1_loss_28: 0.1435 - dense_1_loss_29: 0.1306 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5000 - dense_1_acc_3: 0.7833 - dense_1_acc_4: 0.9667 - 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 58/100
60/60 [==============================] - 0s - loss: 10.3972 - dense_1_loss_1: 3.8923 - dense_1_loss_2: 2.0664 - dense_1_loss_3: 0.8875 - dense_1_loss_4: 0.3601 - dense_1_loss_5: 0.2754 - dense_1_loss_6: 0.1914 - dense_1_loss_7: 0.1572 - dense_1_loss_8: 0.1347 - dense_1_loss_9: 0.1315 - dense_1_loss_10: 0.1170 - dense_1_loss_11: 0.1206 - dense_1_loss_12: 0.1109 - dense_1_loss_13: 0.0949 - dense_1_loss_14: 0.0990 - dense_1_loss_15: 0.1122 - dense_1_loss_16: 0.1091 - dense_1_loss_17: 0.1138 - dense_1_loss_18: 0.1074 - dense_1_loss_19: 0.1039 - dense_1_loss_20: 0.1216 - dense_1_loss_21: 0.1261 - dense_1_loss_22: 0.1103 - dense_1_loss_23: 0.1142 - dense_1_loss_24: 0.1139 - dense_1_loss_25: 0.1196 - dense_1_loss_26: 0.1168 - dense_1_loss_27: 0.1325 - dense_1_loss_28: 0.1334 - dense_1_loss_29: 0.1235 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5000 - dense_1_acc_3: 0.7833 - dense_1_acc_4: 0.9667 - 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 59/100
60/60 [==============================] - 0s - loss: 10.1496 - dense_1_loss_1: 3.8888 - dense_1_loss_2: 2.0375 - dense_1_loss_3: 0.8631 - dense_1_loss_4: 0.3448 - dense_1_loss_5: 0.2613 - dense_1_loss_6: 0.1817 - dense_1_loss_7: 0.1484 - dense_1_loss_8: 0.1280 - dense_1_loss_9: 0.1244 - dense_1_loss_10: 0.1105 - dense_1_loss_11: 0.1142 - dense_1_loss_12: 0.1048 - dense_1_loss_13: 0.0897 - dense_1_loss_14: 0.0933 - dense_1_loss_15: 0.1062 - dense_1_loss_16: 0.1033 - dense_1_loss_17: 0.1073 - dense_1_loss_18: 0.1014 - dense_1_loss_19: 0.0992 - dense_1_loss_20: 0.1140 - dense_1_loss_21: 0.1186 - dense_1_loss_22: 0.1045 - dense_1_loss_23: 0.1085 - dense_1_loss_24: 0.1064 - dense_1_loss_25: 0.1126 - dense_1_loss_26: 0.1093 - dense_1_loss_27: 0.1280 - dense_1_loss_28: 0.1243 - dense_1_loss_29: 0.1156 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5000 - dense_1_acc_3: 0.8000 - 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 60/100
60/60 [==============================] - 0s - loss: 9.9187 - dense_1_loss_1: 3.8849 - dense_1_loss_2: 2.0084 - dense_1_loss_3: 0.8408 - dense_1_loss_4: 0.3300 - dense_1_loss_5: 0.2464 - dense_1_loss_6: 0.1727 - dense_1_loss_7: 0.1394 - dense_1_loss_8: 0.1215 - dense_1_loss_9: 0.1171 - dense_1_loss_10: 0.1042 - dense_1_loss_11: 0.1062 - dense_1_loss_12: 0.0995 - dense_1_loss_13: 0.0850 - dense_1_loss_14: 0.0891 - dense_1_loss_15: 0.0996 - dense_1_loss_16: 0.0989 - dense_1_loss_17: 0.1013 - dense_1_loss_18: 0.0965 - dense_1_loss_19: 0.0943 - dense_1_loss_20: 0.1062 - dense_1_loss_21: 0.1130 - dense_1_loss_22: 0.0989 - dense_1_loss_23: 0.1032 - dense_1_loss_24: 0.1016 - dense_1_loss_25: 0.1074 - dense_1_loss_26: 0.1036 - dense_1_loss_27: 0.1209 - dense_1_loss_28: 0.1186 - dense_1_loss_29: 0.1095 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5000 - dense_1_acc_3: 0.8167 - 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 61/100
60/60 [==============================] - 0s - loss: 9.7048 - dense_1_loss_1: 3.8811 - dense_1_loss_2: 1.9814 - dense_1_loss_3: 0.8196 - dense_1_loss_4: 0.3149 - dense_1_loss_5: 0.2338 - dense_1_loss_6: 0.1651 - dense_1_loss_7: 0.1323 - dense_1_loss_8: 0.1157 - dense_1_loss_9: 0.1115 - dense_1_loss_10: 0.0980 - dense_1_loss_11: 0.0999 - dense_1_loss_12: 0.0945 - dense_1_loss_13: 0.0810 - dense_1_loss_14: 0.0844 - dense_1_loss_15: 0.0936 - dense_1_loss_16: 0.0934 - dense_1_loss_17: 0.0961 - dense_1_loss_18: 0.0918 - dense_1_loss_19: 0.0885 - dense_1_loss_20: 0.0999 - dense_1_loss_21: 0.1077 - dense_1_loss_22: 0.0929 - dense_1_loss_23: 0.0987 - dense_1_loss_24: 0.0972 - dense_1_loss_25: 0.1017 - dense_1_loss_26: 0.0983 - dense_1_loss_27: 0.1132 - dense_1_loss_28: 0.1130 - dense_1_loss_29: 0.1055 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5333 - dense_1_acc_3: 0.8167 - 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 62/100
60/60 [==============================] - 0s - loss: 9.5101 - dense_1_loss_1: 3.8774 - dense_1_loss_2: 1.9538 - dense_1_loss_3: 0.7993 - dense_1_loss_4: 0.3020 - dense_1_loss_5: 0.2226 - dense_1_loss_6: 0.1575 - dense_1_loss_7: 0.1258 - dense_1_loss_8: 0.1103 - dense_1_loss_9: 0.1064 - dense_1_loss_10: 0.0930 - dense_1_loss_11: 0.0954 - dense_1_loss_12: 0.0899 - dense_1_loss_13: 0.0775 - dense_1_loss_14: 0.0801 - dense_1_loss_15: 0.0894 - dense_1_loss_16: 0.0879 - dense_1_loss_17: 0.0921 - dense_1_loss_18: 0.0874 - dense_1_loss_19: 0.0838 - dense_1_loss_20: 0.0956 - dense_1_loss_21: 0.1016 - dense_1_loss_22: 0.0885 - dense_1_loss_23: 0.0938 - dense_1_loss_24: 0.0925 - dense_1_loss_25: 0.0960 - dense_1_loss_26: 0.0934 - dense_1_loss_27: 0.1076 - dense_1_loss_28: 0.1082 - dense_1_loss_29: 0.1012 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5500 - dense_1_acc_3: 0.8167 - 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 63/100
60/60 [==============================] - 0s - loss: 9.3237 - dense_1_loss_1: 3.8739 - dense_1_loss_2: 1.9281 - dense_1_loss_3: 0.7797 - dense_1_loss_4: 0.2887 - dense_1_loss_5: 0.2128 - dense_1_loss_6: 0.1495 - dense_1_loss_7: 0.1202 - dense_1_loss_8: 0.1049 - dense_1_loss_9: 0.1012 - dense_1_loss_10: 0.0880 - dense_1_loss_11: 0.0908 - dense_1_loss_12: 0.0858 - dense_1_loss_13: 0.0736 - dense_1_loss_14: 0.0765 - dense_1_loss_15: 0.0852 - dense_1_loss_16: 0.0832 - dense_1_loss_17: 0.0876 - dense_1_loss_18: 0.0825 - dense_1_loss_19: 0.0800 - dense_1_loss_20: 0.0913 - dense_1_loss_21: 0.0964 - dense_1_loss_22: 0.0840 - dense_1_loss_23: 0.0889 - dense_1_loss_24: 0.0879 - dense_1_loss_25: 0.0905 - dense_1_loss_26: 0.0892 - dense_1_loss_27: 0.1031 - dense_1_loss_28: 0.1027 - dense_1_loss_29: 0.0973 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5500 - dense_1_acc_3: 0.8167 - 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 64/100
60/60 [==============================] - 0s - loss: 9.1449 - dense_1_loss_1: 3.8703 - dense_1_loss_2: 1.9032 - dense_1_loss_3: 0.7600 - dense_1_loss_4: 0.2778 - dense_1_loss_5: 0.2035 - dense_1_loss_6: 0.1414 - dense_1_loss_7: 0.1145 - dense_1_loss_8: 0.1000 - dense_1_loss_9: 0.0955 - dense_1_loss_10: 0.0838 - dense_1_loss_11: 0.0862 - dense_1_loss_12: 0.0813 - dense_1_loss_13: 0.0702 - dense_1_loss_14: 0.0727 - dense_1_loss_15: 0.0814 - dense_1_loss_16: 0.0794 - dense_1_loss_17: 0.0828 - dense_1_loss_18: 0.0781 - dense_1_loss_19: 0.0764 - dense_1_loss_20: 0.0869 - dense_1_loss_21: 0.0922 - dense_1_loss_22: 0.0797 - dense_1_loss_23: 0.0846 - dense_1_loss_24: 0.0837 - dense_1_loss_25: 0.0863 - dense_1_loss_26: 0.0852 - dense_1_loss_27: 0.0989 - dense_1_loss_28: 0.0975 - dense_1_loss_29: 0.0916 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5500 - dense_1_acc_3: 0.8167 - 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 65/100
60/60 [==============================] - 0s - loss: 8.9847 - dense_1_loss_1: 3.8669 - dense_1_loss_2: 1.8778 - dense_1_loss_3: 0.7427 - dense_1_loss_4: 0.2672 - dense_1_loss_5: 0.1947 - dense_1_loss_6: 0.1349 - dense_1_loss_7: 0.1093 - dense_1_loss_8: 0.0955 - dense_1_loss_9: 0.0912 - dense_1_loss_10: 0.0802 - dense_1_loss_11: 0.0822 - dense_1_loss_12: 0.0776 - dense_1_loss_13: 0.0670 - dense_1_loss_14: 0.0697 - dense_1_loss_15: 0.0776 - dense_1_loss_16: 0.0763 - dense_1_loss_17: 0.0786 - dense_1_loss_18: 0.0746 - dense_1_loss_19: 0.0735 - dense_1_loss_20: 0.0829 - dense_1_loss_21: 0.0887 - dense_1_loss_22: 0.0760 - dense_1_loss_23: 0.0808 - dense_1_loss_24: 0.0797 - dense_1_loss_25: 0.0831 - dense_1_loss_26: 0.0813 - dense_1_loss_27: 0.0947 - dense_1_loss_28: 0.0930 - dense_1_loss_29: 0.0869 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5500 - dense_1_acc_3: 0.8333 - 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 66/100
60/60 [==============================] - 0s - loss: 8.8296 - dense_1_loss_1: 3.8635 - dense_1_loss_2: 1.8544 - dense_1_loss_3: 0.7259 - dense_1_loss_4: 0.2562 - dense_1_loss_5: 0.1862 - dense_1_loss_6: 0.1294 - dense_1_loss_7: 0.1047 - dense_1_loss_8: 0.0914 - dense_1_loss_9: 0.0873 - dense_1_loss_10: 0.0763 - dense_1_loss_11: 0.0783 - dense_1_loss_12: 0.0743 - dense_1_loss_13: 0.0640 - dense_1_loss_14: 0.0667 - dense_1_loss_15: 0.0739 - dense_1_loss_16: 0.0730 - dense_1_loss_17: 0.0747 - dense_1_loss_18: 0.0713 - dense_1_loss_19: 0.0698 - dense_1_loss_20: 0.0792 - dense_1_loss_21: 0.0837 - dense_1_loss_22: 0.0730 - dense_1_loss_23: 0.0770 - dense_1_loss_24: 0.0760 - dense_1_loss_25: 0.0800 - dense_1_loss_26: 0.0775 - dense_1_loss_27: 0.0901 - dense_1_loss_28: 0.0880 - dense_1_loss_29: 0.0837 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5500 - dense_1_acc_3: 0.8500 - 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 67/100
60/60 [==============================] - 0s - loss: 8.6870 - dense_1_loss_1: 3.8599 - dense_1_loss_2: 1.8310 - dense_1_loss_3: 0.7101 - dense_1_loss_4: 0.2463 - dense_1_loss_5: 0.1787 - dense_1_loss_6: 0.1242 - dense_1_loss_7: 0.1004 - dense_1_loss_8: 0.0880 - dense_1_loss_9: 0.0844 - dense_1_loss_10: 0.0729 - dense_1_loss_11: 0.0749 - dense_1_loss_12: 0.0714 - dense_1_loss_13: 0.0612 - dense_1_loss_14: 0.0638 - dense_1_loss_15: 0.0707 - dense_1_loss_16: 0.0698 - dense_1_loss_17: 0.0719 - dense_1_loss_18: 0.0685 - dense_1_loss_19: 0.0662 - dense_1_loss_20: 0.0757 - dense_1_loss_21: 0.0793 - dense_1_loss_22: 0.0700 - dense_1_loss_23: 0.0735 - dense_1_loss_24: 0.0725 - dense_1_loss_25: 0.0765 - dense_1_loss_26: 0.0742 - dense_1_loss_27: 0.0858 - dense_1_loss_28: 0.0842 - dense_1_loss_29: 0.0810 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5667 - dense_1_acc_3: 0.8500 - 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 68/100
60/60 [==============================] - 0s - loss: 8.5524 - dense_1_loss_1: 3.8566 - dense_1_loss_2: 1.8083 - dense_1_loss_3: 0.6939 - dense_1_loss_4: 0.2374 - dense_1_loss_5: 0.1712 - dense_1_loss_6: 0.1198 - dense_1_loss_7: 0.0960 - dense_1_loss_8: 0.0845 - dense_1_loss_9: 0.0810 - dense_1_loss_10: 0.0699 - dense_1_loss_11: 0.0716 - dense_1_loss_12: 0.0686 - dense_1_loss_13: 0.0587 - dense_1_loss_14: 0.0611 - dense_1_loss_15: 0.0675 - dense_1_loss_16: 0.0668 - dense_1_loss_17: 0.0694 - dense_1_loss_18: 0.0655 - dense_1_loss_19: 0.0633 - dense_1_loss_20: 0.0726 - dense_1_loss_21: 0.0760 - dense_1_loss_22: 0.0672 - dense_1_loss_23: 0.0704 - dense_1_loss_24: 0.0698 - dense_1_loss_25: 0.0730 - dense_1_loss_26: 0.0714 - dense_1_loss_27: 0.0820 - dense_1_loss_28: 0.0809 - dense_1_loss_29: 0.0780 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5667 - dense_1_acc_3: 0.8500 - 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: 8.4232 - dense_1_loss_1: 3.8534 - dense_1_loss_2: 1.7868 - dense_1_loss_3: 0.6792 - dense_1_loss_4: 0.2288 - dense_1_loss_5: 0.1643 - dense_1_loss_6: 0.1145 - dense_1_loss_7: 0.0918 - dense_1_loss_8: 0.0808 - dense_1_loss_9: 0.0774 - dense_1_loss_10: 0.0674 - dense_1_loss_11: 0.0676 - dense_1_loss_12: 0.0656 - dense_1_loss_13: 0.0564 - dense_1_loss_14: 0.0583 - dense_1_loss_15: 0.0647 - dense_1_loss_16: 0.0645 - dense_1_loss_17: 0.0669 - dense_1_loss_18: 0.0626 - dense_1_loss_19: 0.0605 - dense_1_loss_20: 0.0692 - dense_1_loss_21: 0.0740 - dense_1_loss_22: 0.0637 - dense_1_loss_23: 0.0677 - dense_1_loss_24: 0.0677 - dense_1_loss_25: 0.0697 - dense_1_loss_26: 0.0685 - dense_1_loss_27: 0.0789 - dense_1_loss_28: 0.0778 - dense_1_loss_29: 0.0744 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - 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: 8.3022 - dense_1_loss_1: 3.8501 - dense_1_loss_2: 1.7648 - dense_1_loss_3: 0.6645 - dense_1_loss_4: 0.2207 - dense_1_loss_5: 0.1578 - dense_1_loss_6: 0.1099 - dense_1_loss_7: 0.0884 - dense_1_loss_8: 0.0774 - dense_1_loss_9: 0.0745 - dense_1_loss_10: 0.0649 - dense_1_loss_11: 0.0645 - dense_1_loss_12: 0.0633 - dense_1_loss_13: 0.0538 - dense_1_loss_14: 0.0561 - dense_1_loss_15: 0.0622 - dense_1_loss_16: 0.0620 - dense_1_loss_17: 0.0643 - dense_1_loss_18: 0.0601 - dense_1_loss_19: 0.0584 - dense_1_loss_20: 0.0665 - dense_1_loss_21: 0.0713 - dense_1_loss_22: 0.0612 - dense_1_loss_23: 0.0650 - dense_1_loss_24: 0.0652 - dense_1_loss_25: 0.0670 - dense_1_loss_26: 0.0659 - dense_1_loss_27: 0.0762 - dense_1_loss_28: 0.0751 - dense_1_loss_29: 0.0712 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - 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: 8.1875 - dense_1_loss_1: 3.8468 - dense_1_loss_2: 1.7445 - dense_1_loss_3: 0.6511 - dense_1_loss_4: 0.2120 - dense_1_loss_5: 0.1518 - dense_1_loss_6: 0.1057 - dense_1_loss_7: 0.0854 - dense_1_loss_8: 0.0744 - dense_1_loss_9: 0.0717 - dense_1_loss_10: 0.0623 - dense_1_loss_11: 0.0622 - dense_1_loss_12: 0.0609 - dense_1_loss_13: 0.0518 - dense_1_loss_14: 0.0538 - dense_1_loss_15: 0.0600 - dense_1_loss_16: 0.0597 - dense_1_loss_17: 0.0617 - dense_1_loss_18: 0.0576 - dense_1_loss_19: 0.0564 - dense_1_loss_20: 0.0639 - dense_1_loss_21: 0.0677 - dense_1_loss_22: 0.0593 - dense_1_loss_23: 0.0626 - dense_1_loss_24: 0.0625 - dense_1_loss_25: 0.0644 - dense_1_loss_26: 0.0632 - dense_1_loss_27: 0.0738 - dense_1_loss_28: 0.0718 - dense_1_loss_29: 0.0687 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - 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: 8.0787 - dense_1_loss_1: 3.8438 - dense_1_loss_2: 1.7239 - dense_1_loss_3: 0.6373 - dense_1_loss_4: 0.2054 - dense_1_loss_5: 0.1465 - dense_1_loss_6: 0.1017 - dense_1_loss_7: 0.0825 - dense_1_loss_8: 0.0717 - dense_1_loss_9: 0.0692 - dense_1_loss_10: 0.0598 - dense_1_loss_11: 0.0600 - dense_1_loss_12: 0.0586 - dense_1_loss_13: 0.0498 - dense_1_loss_14: 0.0519 - dense_1_loss_15: 0.0580 - dense_1_loss_16: 0.0573 - dense_1_loss_17: 0.0592 - dense_1_loss_18: 0.0555 - dense_1_loss_19: 0.0541 - dense_1_loss_20: 0.0615 - dense_1_loss_21: 0.0650 - dense_1_loss_22: 0.0571 - dense_1_loss_23: 0.0602 - dense_1_loss_24: 0.0601 - dense_1_loss_25: 0.0621 - dense_1_loss_26: 0.0607 - dense_1_loss_27: 0.0709 - dense_1_loss_28: 0.0687 - dense_1_loss_29: 0.0663 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - 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: 7.9776 - dense_1_loss_1: 3.8404 - dense_1_loss_2: 1.7045 - dense_1_loss_3: 0.6251 - dense_1_loss_4: 0.1982 - dense_1_loss_5: 0.1418 - dense_1_loss_6: 0.0982 - dense_1_loss_7: 0.0798 - dense_1_loss_8: 0.0693 - dense_1_loss_9: 0.0668 - dense_1_loss_10: 0.0576 - dense_1_loss_11: 0.0578 - dense_1_loss_12: 0.0566 - dense_1_loss_13: 0.0482 - dense_1_loss_14: 0.0501 - dense_1_loss_15: 0.0561 - dense_1_loss_16: 0.0552 - dense_1_loss_17: 0.0571 - dense_1_loss_18: 0.0534 - dense_1_loss_19: 0.0522 - dense_1_loss_20: 0.0592 - dense_1_loss_21: 0.0627 - dense_1_loss_22: 0.0547 - dense_1_loss_23: 0.0581 - dense_1_loss_24: 0.0578 - dense_1_loss_25: 0.0599 - dense_1_loss_26: 0.0584 - dense_1_loss_27: 0.0681 - dense_1_loss_28: 0.0660 - dense_1_loss_29: 0.0640 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - 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: 7.8771 - dense_1_loss_1: 3.8371 - dense_1_loss_2: 1.6855 - dense_1_loss_3: 0.6122 - dense_1_loss_4: 0.1911 - dense_1_loss_5: 0.1367 - dense_1_loss_6: 0.0945 - dense_1_loss_7: 0.0769 - dense_1_loss_8: 0.0668 - dense_1_loss_9: 0.0643 - dense_1_loss_10: 0.0554 - dense_1_loss_11: 0.0556 - dense_1_loss_12: 0.0547 - dense_1_loss_13: 0.0466 - dense_1_loss_14: 0.0483 - dense_1_loss_15: 0.0542 - dense_1_loss_16: 0.0532 - dense_1_loss_17: 0.0549 - dense_1_loss_18: 0.0516 - dense_1_loss_19: 0.0502 - dense_1_loss_20: 0.0570 - dense_1_loss_21: 0.0606 - dense_1_loss_22: 0.0525 - dense_1_loss_23: 0.0561 - dense_1_loss_24: 0.0557 - dense_1_loss_25: 0.0579 - dense_1_loss_26: 0.0564 - dense_1_loss_27: 0.0654 - dense_1_loss_28: 0.0637 - dense_1_loss_29: 0.0619 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - 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: 7.7843 - dense_1_loss_1: 3.8343 - dense_1_loss_2: 1.6666 - dense_1_loss_3: 0.5999 - dense_1_loss_4: 0.1852 - dense_1_loss_5: 0.1319 - dense_1_loss_6: 0.0912 - dense_1_loss_7: 0.0742 - dense_1_loss_8: 0.0646 - dense_1_loss_9: 0.0622 - dense_1_loss_10: 0.0536 - dense_1_loss_11: 0.0534 - dense_1_loss_12: 0.0529 - dense_1_loss_13: 0.0449 - dense_1_loss_14: 0.0467 - dense_1_loss_15: 0.0522 - dense_1_loss_16: 0.0513 - dense_1_loss_17: 0.0531 - dense_1_loss_18: 0.0499 - dense_1_loss_19: 0.0486 - dense_1_loss_20: 0.0549 - dense_1_loss_21: 0.0586 - dense_1_loss_22: 0.0507 - dense_1_loss_23: 0.0542 - dense_1_loss_24: 0.0539 - dense_1_loss_25: 0.0560 - dense_1_loss_26: 0.0546 - dense_1_loss_27: 0.0631 - dense_1_loss_28: 0.0617 - dense_1_loss_29: 0.0600 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - 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: 7.6950 - dense_1_loss_1: 3.8312 - dense_1_loss_2: 1.6486 - dense_1_loss_3: 0.5883 - dense_1_loss_4: 0.1791 - dense_1_loss_5: 0.1273 - dense_1_loss_6: 0.0882 - dense_1_loss_7: 0.0717 - dense_1_loss_8: 0.0627 - dense_1_loss_9: 0.0602 - dense_1_loss_10: 0.0519 - dense_1_loss_11: 0.0515 - dense_1_loss_12: 0.0511 - dense_1_loss_13: 0.0434 - dense_1_loss_14: 0.0452 - dense_1_loss_15: 0.0505 - dense_1_loss_16: 0.0496 - dense_1_loss_17: 0.0514 - dense_1_loss_18: 0.0481 - dense_1_loss_19: 0.0472 - dense_1_loss_20: 0.0530 - dense_1_loss_21: 0.0566 - dense_1_loss_22: 0.0491 - dense_1_loss_23: 0.0523 - dense_1_loss_24: 0.0520 - dense_1_loss_25: 0.0541 - dense_1_loss_26: 0.0528 - dense_1_loss_27: 0.0605 - dense_1_loss_28: 0.0595 - dense_1_loss_29: 0.0579 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - 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: 7.6123 - dense_1_loss_1: 3.8282 - dense_1_loss_2: 1.6309 - dense_1_loss_3: 0.5771 - dense_1_loss_4: 0.1739 - dense_1_loss_5: 0.1235 - dense_1_loss_6: 0.0855 - dense_1_loss_7: 0.0696 - dense_1_loss_8: 0.0607 - dense_1_loss_9: 0.0584 - dense_1_loss_10: 0.0502 - dense_1_loss_11: 0.0499 - dense_1_loss_12: 0.0496 - dense_1_loss_13: 0.0419 - dense_1_loss_14: 0.0438 - dense_1_loss_15: 0.0489 - dense_1_loss_16: 0.0480 - dense_1_loss_17: 0.0498 - dense_1_loss_18: 0.0466 - dense_1_loss_19: 0.0457 - dense_1_loss_20: 0.0513 - dense_1_loss_21: 0.0545 - dense_1_loss_22: 0.0476 - dense_1_loss_23: 0.0507 - dense_1_loss_24: 0.0504 - dense_1_loss_25: 0.0522 - dense_1_loss_26: 0.0513 - dense_1_loss_27: 0.0586 - dense_1_loss_28: 0.0573 - dense_1_loss_29: 0.0562 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - 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: 7.5311 - dense_1_loss_1: 3.8250 - dense_1_loss_2: 1.6137 - dense_1_loss_3: 0.5659 - dense_1_loss_4: 0.1689 - dense_1_loss_5: 0.1198 - dense_1_loss_6: 0.0828 - dense_1_loss_7: 0.0675 - dense_1_loss_8: 0.0587 - dense_1_loss_9: 0.0565 - dense_1_loss_10: 0.0486 - dense_1_loss_11: 0.0482 - dense_1_loss_12: 0.0479 - dense_1_loss_13: 0.0407 - dense_1_loss_14: 0.0422 - dense_1_loss_15: 0.0474 - dense_1_loss_16: 0.0466 - dense_1_loss_17: 0.0483 - dense_1_loss_18: 0.0450 - dense_1_loss_19: 0.0441 - dense_1_loss_20: 0.0496 - dense_1_loss_21: 0.0523 - dense_1_loss_22: 0.0462 - dense_1_loss_23: 0.0491 - dense_1_loss_24: 0.0488 - dense_1_loss_25: 0.0505 - dense_1_loss_26: 0.0497 - dense_1_loss_27: 0.0570 - dense_1_loss_28: 0.0556 - dense_1_loss_29: 0.0545 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8667 - 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: 7.4521 - dense_1_loss_1: 3.8223 - dense_1_loss_2: 1.5973 - dense_1_loss_3: 0.5539 - dense_1_loss_4: 0.1637 - dense_1_loss_5: 0.1160 - dense_1_loss_6: 0.0801 - dense_1_loss_7: 0.0654 - dense_1_loss_8: 0.0569 - dense_1_loss_9: 0.0546 - dense_1_loss_10: 0.0471 - dense_1_loss_11: 0.0467 - dense_1_loss_12: 0.0464 - dense_1_loss_13: 0.0394 - dense_1_loss_14: 0.0408 - dense_1_loss_15: 0.0460 - dense_1_loss_16: 0.0453 - dense_1_loss_17: 0.0467 - dense_1_loss_18: 0.0436 - dense_1_loss_19: 0.0427 - dense_1_loss_20: 0.0479 - dense_1_loss_21: 0.0507 - dense_1_loss_22: 0.0446 - dense_1_loss_23: 0.0477 - dense_1_loss_24: 0.0474 - dense_1_loss_25: 0.0489 - dense_1_loss_26: 0.0483 - dense_1_loss_27: 0.0551 - dense_1_loss_28: 0.0538 - dense_1_loss_29: 0.0528 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8667 - 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 80/100
60/60 [==============================] - 0s - loss: 7.3783 - dense_1_loss_1: 3.8193 - dense_1_loss_2: 1.5801 - dense_1_loss_3: 0.5444 - dense_1_loss_4: 0.1593 - dense_1_loss_5: 0.1130 - dense_1_loss_6: 0.0778 - dense_1_loss_7: 0.0636 - dense_1_loss_8: 0.0553 - dense_1_loss_9: 0.0531 - dense_1_loss_10: 0.0457 - dense_1_loss_11: 0.0452 - dense_1_loss_12: 0.0450 - dense_1_loss_13: 0.0383 - dense_1_loss_14: 0.0396 - dense_1_loss_15: 0.0446 - dense_1_loss_16: 0.0440 - dense_1_loss_17: 0.0453 - dense_1_loss_18: 0.0423 - dense_1_loss_19: 0.0414 - dense_1_loss_20: 0.0464 - dense_1_loss_21: 0.0493 - dense_1_loss_22: 0.0431 - dense_1_loss_23: 0.0464 - dense_1_loss_24: 0.0459 - dense_1_loss_25: 0.0474 - dense_1_loss_26: 0.0467 - dense_1_loss_27: 0.0530 - dense_1_loss_28: 0.0519 - dense_1_loss_29: 0.0510 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8667 - 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 81/100
60/60 [==============================] - 0s - loss: 7.3064 - dense_1_loss_1: 3.8165 - dense_1_loss_2: 1.5643 - dense_1_loss_3: 0.5338 - dense_1_loss_4: 0.1547 - dense_1_loss_5: 0.1095 - dense_1_loss_6: 0.0753 - dense_1_loss_7: 0.0617 - dense_1_loss_8: 0.0535 - dense_1_loss_9: 0.0516 - dense_1_loss_10: 0.0442 - dense_1_loss_11: 0.0437 - dense_1_loss_12: 0.0437 - dense_1_loss_13: 0.0370 - dense_1_loss_14: 0.0386 - dense_1_loss_15: 0.0432 - dense_1_loss_16: 0.0425 - dense_1_loss_17: 0.0439 - dense_1_loss_18: 0.0411 - dense_1_loss_19: 0.0402 - dense_1_loss_20: 0.0451 - dense_1_loss_21: 0.0478 - dense_1_loss_22: 0.0419 - dense_1_loss_23: 0.0449 - dense_1_loss_24: 0.0447 - dense_1_loss_25: 0.0459 - dense_1_loss_26: 0.0455 - dense_1_loss_27: 0.0516 - dense_1_loss_28: 0.0505 - 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: 0.8833 - 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: 7.2396 - dense_1_loss_1: 3.8135 - dense_1_loss_2: 1.5489 - dense_1_loss_3: 0.5244 - dense_1_loss_4: 0.1508 - dense_1_loss_5: 0.1065 - dense_1_loss_6: 0.0733 - dense_1_loss_7: 0.0601 - dense_1_loss_8: 0.0522 - dense_1_loss_9: 0.0502 - dense_1_loss_10: 0.0429 - dense_1_loss_11: 0.0424 - dense_1_loss_12: 0.0424 - dense_1_loss_13: 0.0360 - dense_1_loss_14: 0.0376 - dense_1_loss_15: 0.0420 - dense_1_loss_16: 0.0413 - dense_1_loss_17: 0.0427 - dense_1_loss_18: 0.0400 - dense_1_loss_19: 0.0390 - dense_1_loss_20: 0.0437 - dense_1_loss_21: 0.0464 - dense_1_loss_22: 0.0406 - dense_1_loss_23: 0.0436 - dense_1_loss_24: 0.0434 - dense_1_loss_25: 0.0446 - dense_1_loss_26: 0.0441 - dense_1_loss_27: 0.0497 - dense_1_loss_28: 0.0491 - 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: 0.8833 - 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: 7.1718 - dense_1_loss_1: 3.8107 - dense_1_loss_2: 1.5326 - dense_1_loss_3: 0.5147 - dense_1_loss_4: 0.1462 - dense_1_loss_5: 0.1036 - dense_1_loss_6: 0.0711 - dense_1_loss_7: 0.0583 - dense_1_loss_8: 0.0505 - dense_1_loss_9: 0.0487 - dense_1_loss_10: 0.0415 - dense_1_loss_11: 0.0412 - dense_1_loss_12: 0.0412 - dense_1_loss_13: 0.0349 - dense_1_loss_14: 0.0366 - dense_1_loss_15: 0.0408 - dense_1_loss_16: 0.0401 - dense_1_loss_17: 0.0415 - dense_1_loss_18: 0.0388 - dense_1_loss_19: 0.0379 - dense_1_loss_20: 0.0424 - dense_1_loss_21: 0.0451 - dense_1_loss_22: 0.0394 - dense_1_loss_23: 0.0423 - dense_1_loss_24: 0.0423 - dense_1_loss_25: 0.0433 - dense_1_loss_26: 0.0428 - dense_1_loss_27: 0.0483 - dense_1_loss_28: 0.0478 - dense_1_loss_29: 0.0471 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8833 - 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: 7.1095 - dense_1_loss_1: 3.8079 - dense_1_loss_2: 1.5175 - dense_1_loss_3: 0.5055 - dense_1_loss_4: 0.1428 - dense_1_loss_5: 0.1009 - dense_1_loss_6: 0.0692 - dense_1_loss_7: 0.0569 - dense_1_loss_8: 0.0493 - dense_1_loss_9: 0.0473 - dense_1_loss_10: 0.0404 - dense_1_loss_11: 0.0400 - dense_1_loss_12: 0.0401 - dense_1_loss_13: 0.0340 - dense_1_loss_14: 0.0355 - dense_1_loss_15: 0.0397 - dense_1_loss_16: 0.0390 - dense_1_loss_17: 0.0404 - dense_1_loss_18: 0.0378 - dense_1_loss_19: 0.0368 - dense_1_loss_20: 0.0411 - dense_1_loss_21: 0.0438 - dense_1_loss_22: 0.0383 - dense_1_loss_23: 0.0411 - dense_1_loss_24: 0.0411 - dense_1_loss_25: 0.0421 - dense_1_loss_26: 0.0416 - dense_1_loss_27: 0.0470 - dense_1_loss_28: 0.0466 - dense_1_loss_29: 0.0459 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 7.0478 - dense_1_loss_1: 3.8051 - dense_1_loss_2: 1.5033 - dense_1_loss_3: 0.4954 - dense_1_loss_4: 0.1393 - dense_1_loss_5: 0.0980 - dense_1_loss_6: 0.0674 - dense_1_loss_7: 0.0553 - dense_1_loss_8: 0.0480 - dense_1_loss_9: 0.0460 - dense_1_loss_10: 0.0394 - dense_1_loss_11: 0.0389 - dense_1_loss_12: 0.0391 - dense_1_loss_13: 0.0331 - dense_1_loss_14: 0.0345 - dense_1_loss_15: 0.0385 - dense_1_loss_16: 0.0381 - dense_1_loss_17: 0.0393 - dense_1_loss_18: 0.0367 - dense_1_loss_19: 0.0358 - dense_1_loss_20: 0.0400 - dense_1_loss_21: 0.0425 - dense_1_loss_22: 0.0373 - dense_1_loss_23: 0.0399 - dense_1_loss_24: 0.0400 - dense_1_loss_25: 0.0410 - dense_1_loss_26: 0.0404 - dense_1_loss_27: 0.0457 - dense_1_loss_28: 0.0451 - dense_1_loss_29: 0.0446 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.9901 - dense_1_loss_1: 3.8025 - dense_1_loss_2: 1.4886 - dense_1_loss_3: 0.4869 - dense_1_loss_4: 0.1360 - dense_1_loss_5: 0.0955 - dense_1_loss_6: 0.0658 - dense_1_loss_7: 0.0539 - dense_1_loss_8: 0.0468 - dense_1_loss_9: 0.0448 - dense_1_loss_10: 0.0384 - dense_1_loss_11: 0.0378 - dense_1_loss_12: 0.0381 - dense_1_loss_13: 0.0323 - dense_1_loss_14: 0.0336 - dense_1_loss_15: 0.0376 - dense_1_loss_16: 0.0372 - dense_1_loss_17: 0.0383 - dense_1_loss_18: 0.0357 - dense_1_loss_19: 0.0350 - dense_1_loss_20: 0.0390 - dense_1_loss_21: 0.0412 - dense_1_loss_22: 0.0364 - dense_1_loss_23: 0.0388 - dense_1_loss_24: 0.0389 - dense_1_loss_25: 0.0399 - dense_1_loss_26: 0.0392 - dense_1_loss_27: 0.0445 - dense_1_loss_28: 0.0438 - dense_1_loss_29: 0.0434 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.9341 - dense_1_loss_1: 3.7994 - dense_1_loss_2: 1.4752 - dense_1_loss_3: 0.4786 - dense_1_loss_4: 0.1326 - dense_1_loss_5: 0.0933 - dense_1_loss_6: 0.0642 - dense_1_loss_7: 0.0527 - dense_1_loss_8: 0.0456 - dense_1_loss_9: 0.0438 - dense_1_loss_10: 0.0374 - dense_1_loss_11: 0.0368 - dense_1_loss_12: 0.0372 - dense_1_loss_13: 0.0314 - dense_1_loss_14: 0.0326 - dense_1_loss_15: 0.0366 - dense_1_loss_16: 0.0362 - dense_1_loss_17: 0.0373 - dense_1_loss_18: 0.0348 - dense_1_loss_19: 0.0340 - dense_1_loss_20: 0.0380 - dense_1_loss_21: 0.0401 - dense_1_loss_22: 0.0354 - dense_1_loss_23: 0.0378 - dense_1_loss_24: 0.0381 - dense_1_loss_25: 0.0388 - dense_1_loss_26: 0.0382 - dense_1_loss_27: 0.0433 - dense_1_loss_28: 0.0427 - dense_1_loss_29: 0.0422 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.8787 - dense_1_loss_1: 3.7967 - dense_1_loss_2: 1.4611 - dense_1_loss_3: 0.4699 - dense_1_loss_4: 0.1297 - dense_1_loss_5: 0.0909 - dense_1_loss_6: 0.0624 - dense_1_loss_7: 0.0514 - dense_1_loss_8: 0.0444 - dense_1_loss_9: 0.0427 - dense_1_loss_10: 0.0365 - dense_1_loss_11: 0.0357 - dense_1_loss_12: 0.0362 - dense_1_loss_13: 0.0306 - dense_1_loss_14: 0.0318 - dense_1_loss_15: 0.0357 - dense_1_loss_16: 0.0352 - dense_1_loss_17: 0.0364 - dense_1_loss_18: 0.0339 - dense_1_loss_19: 0.0331 - dense_1_loss_20: 0.0370 - dense_1_loss_21: 0.0391 - dense_1_loss_22: 0.0343 - dense_1_loss_23: 0.0369 - dense_1_loss_24: 0.0372 - dense_1_loss_25: 0.0377 - dense_1_loss_26: 0.0371 - dense_1_loss_27: 0.0422 - dense_1_loss_28: 0.0418 - dense_1_loss_29: 0.0410 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.8258 - dense_1_loss_1: 3.7939 - dense_1_loss_2: 1.4474 - dense_1_loss_3: 0.4616 - dense_1_loss_4: 0.1266 - dense_1_loss_5: 0.0888 - dense_1_loss_6: 0.0607 - dense_1_loss_7: 0.0502 - dense_1_loss_8: 0.0433 - dense_1_loss_9: 0.0415 - dense_1_loss_10: 0.0357 - dense_1_loss_11: 0.0347 - dense_1_loss_12: 0.0353 - dense_1_loss_13: 0.0299 - dense_1_loss_14: 0.0310 - dense_1_loss_15: 0.0348 - dense_1_loss_16: 0.0343 - dense_1_loss_17: 0.0355 - dense_1_loss_18: 0.0330 - dense_1_loss_19: 0.0323 - dense_1_loss_20: 0.0360 - dense_1_loss_21: 0.0383 - dense_1_loss_22: 0.0334 - dense_1_loss_23: 0.0360 - dense_1_loss_24: 0.0363 - dense_1_loss_25: 0.0368 - dense_1_loss_26: 0.0362 - dense_1_loss_27: 0.0412 - dense_1_loss_28: 0.0408 - dense_1_loss_29: 0.0401 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.7761 - dense_1_loss_1: 3.7913 - dense_1_loss_2: 1.4347 - dense_1_loss_3: 0.4540 - dense_1_loss_4: 0.1234 - dense_1_loss_5: 0.0870 - dense_1_loss_6: 0.0592 - dense_1_loss_7: 0.0492 - dense_1_loss_8: 0.0424 - dense_1_loss_9: 0.0406 - dense_1_loss_10: 0.0348 - dense_1_loss_11: 0.0339 - dense_1_loss_12: 0.0345 - dense_1_loss_13: 0.0291 - dense_1_loss_14: 0.0304 - dense_1_loss_15: 0.0339 - dense_1_loss_16: 0.0335 - dense_1_loss_17: 0.0346 - dense_1_loss_18: 0.0323 - dense_1_loss_19: 0.0315 - dense_1_loss_20: 0.0351 - dense_1_loss_21: 0.0371 - dense_1_loss_22: 0.0327 - dense_1_loss_23: 0.0351 - dense_1_loss_24: 0.0353 - dense_1_loss_25: 0.0361 - dense_1_loss_26: 0.0353 - dense_1_loss_27: 0.0401 - dense_1_loss_28: 0.0398 - dense_1_loss_29: 0.0392 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.7272 - dense_1_loss_1: 3.7888 - dense_1_loss_2: 1.4218 - dense_1_loss_3: 0.4466 - dense_1_loss_4: 0.1206 - dense_1_loss_5: 0.0850 - dense_1_loss_6: 0.0577 - dense_1_loss_7: 0.0481 - dense_1_loss_8: 0.0415 - dense_1_loss_9: 0.0396 - dense_1_loss_10: 0.0339 - dense_1_loss_11: 0.0331 - dense_1_loss_12: 0.0337 - dense_1_loss_13: 0.0284 - dense_1_loss_14: 0.0296 - dense_1_loss_15: 0.0332 - dense_1_loss_16: 0.0327 - dense_1_loss_17: 0.0338 - dense_1_loss_18: 0.0315 - dense_1_loss_19: 0.0308 - dense_1_loss_20: 0.0343 - dense_1_loss_21: 0.0360 - dense_1_loss_22: 0.0321 - dense_1_loss_23: 0.0341 - dense_1_loss_24: 0.0344 - dense_1_loss_25: 0.0352 - dense_1_loss_26: 0.0344 - dense_1_loss_27: 0.0391 - dense_1_loss_28: 0.0388 - dense_1_loss_29: 0.0384 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.6787 - dense_1_loss_1: 3.7860 - dense_1_loss_2: 1.4089 - dense_1_loss_3: 0.4388 - dense_1_loss_4: 0.1180 - dense_1_loss_5: 0.0833 - dense_1_loss_6: 0.0563 - dense_1_loss_7: 0.0470 - dense_1_loss_8: 0.0406 - dense_1_loss_9: 0.0386 - dense_1_loss_10: 0.0331 - dense_1_loss_11: 0.0324 - dense_1_loss_12: 0.0329 - dense_1_loss_13: 0.0277 - dense_1_loss_14: 0.0289 - dense_1_loss_15: 0.0324 - dense_1_loss_16: 0.0319 - dense_1_loss_17: 0.0330 - dense_1_loss_18: 0.0308 - dense_1_loss_19: 0.0301 - dense_1_loss_20: 0.0334 - dense_1_loss_21: 0.0351 - dense_1_loss_22: 0.0312 - dense_1_loss_23: 0.0333 - dense_1_loss_24: 0.0336 - dense_1_loss_25: 0.0344 - dense_1_loss_26: 0.0335 - dense_1_loss_27: 0.0382 - dense_1_loss_28: 0.0378 - dense_1_loss_29: 0.0374 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.6333 - dense_1_loss_1: 3.7833 - dense_1_loss_2: 1.3969 - dense_1_loss_3: 0.4317 - dense_1_loss_4: 0.1156 - dense_1_loss_5: 0.0816 - dense_1_loss_6: 0.0550 - dense_1_loss_7: 0.0460 - dense_1_loss_8: 0.0397 - dense_1_loss_9: 0.0377 - dense_1_loss_10: 0.0324 - dense_1_loss_11: 0.0316 - dense_1_loss_12: 0.0321 - dense_1_loss_13: 0.0271 - dense_1_loss_14: 0.0282 - dense_1_loss_15: 0.0317 - dense_1_loss_16: 0.0312 - dense_1_loss_17: 0.0322 - dense_1_loss_18: 0.0301 - dense_1_loss_19: 0.0294 - dense_1_loss_20: 0.0327 - dense_1_loss_21: 0.0344 - dense_1_loss_22: 0.0305 - dense_1_loss_23: 0.0326 - dense_1_loss_24: 0.0328 - dense_1_loss_25: 0.0335 - dense_1_loss_26: 0.0328 - dense_1_loss_27: 0.0374 - dense_1_loss_28: 0.0370 - dense_1_loss_29: 0.0365 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.5888 - dense_1_loss_1: 3.7810 - dense_1_loss_2: 1.3846 - dense_1_loss_3: 0.4243 - dense_1_loss_4: 0.1132 - dense_1_loss_5: 0.0798 - dense_1_loss_6: 0.0538 - dense_1_loss_7: 0.0450 - dense_1_loss_8: 0.0388 - dense_1_loss_9: 0.0369 - dense_1_loss_10: 0.0317 - dense_1_loss_11: 0.0308 - dense_1_loss_12: 0.0315 - dense_1_loss_13: 0.0265 - dense_1_loss_14: 0.0275 - dense_1_loss_15: 0.0309 - dense_1_loss_16: 0.0305 - dense_1_loss_17: 0.0315 - dense_1_loss_18: 0.0293 - dense_1_loss_19: 0.0287 - dense_1_loss_20: 0.0319 - dense_1_loss_21: 0.0337 - dense_1_loss_22: 0.0297 - dense_1_loss_23: 0.0319 - dense_1_loss_24: 0.0321 - dense_1_loss_25: 0.0326 - dense_1_loss_26: 0.0321 - dense_1_loss_27: 0.0366 - dense_1_loss_28: 0.0362 - dense_1_loss_29: 0.0355 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.5456 - dense_1_loss_1: 3.7782 - dense_1_loss_2: 1.3730 - dense_1_loss_3: 0.4167 - dense_1_loss_4: 0.1109 - dense_1_loss_5: 0.0783 - dense_1_loss_6: 0.0526 - dense_1_loss_7: 0.0441 - dense_1_loss_8: 0.0380 - dense_1_loss_9: 0.0360 - dense_1_loss_10: 0.0310 - dense_1_loss_11: 0.0301 - dense_1_loss_12: 0.0307 - dense_1_loss_13: 0.0259 - dense_1_loss_14: 0.0269 - dense_1_loss_15: 0.0302 - dense_1_loss_16: 0.0299 - dense_1_loss_17: 0.0309 - dense_1_loss_18: 0.0287 - dense_1_loss_19: 0.0280 - dense_1_loss_20: 0.0313 - dense_1_loss_21: 0.0330 - dense_1_loss_22: 0.0291 - dense_1_loss_23: 0.0312 - dense_1_loss_24: 0.0315 - dense_1_loss_25: 0.0319 - dense_1_loss_26: 0.0314 - dense_1_loss_27: 0.0359 - dense_1_loss_28: 0.0355 - 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: 0.8833 - 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: 6.5034 - dense_1_loss_1: 3.7757 - dense_1_loss_2: 1.3609 - dense_1_loss_3: 0.4102 - dense_1_loss_4: 0.1086 - dense_1_loss_5: 0.0767 - dense_1_loss_6: 0.0515 - dense_1_loss_7: 0.0432 - dense_1_loss_8: 0.0372 - dense_1_loss_9: 0.0353 - dense_1_loss_10: 0.0303 - dense_1_loss_11: 0.0295 - dense_1_loss_12: 0.0301 - dense_1_loss_13: 0.0253 - dense_1_loss_14: 0.0264 - dense_1_loss_15: 0.0295 - dense_1_loss_16: 0.0293 - dense_1_loss_17: 0.0302 - dense_1_loss_18: 0.0281 - dense_1_loss_19: 0.0274 - dense_1_loss_20: 0.0305 - dense_1_loss_21: 0.0321 - dense_1_loss_22: 0.0285 - dense_1_loss_23: 0.0304 - dense_1_loss_24: 0.0308 - dense_1_loss_25: 0.0312 - dense_1_loss_26: 0.0307 - dense_1_loss_27: 0.0350 - dense_1_loss_28: 0.0347 - dense_1_loss_29: 0.0342 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.4620 - dense_1_loss_1: 3.7731 - dense_1_loss_2: 1.3492 - dense_1_loss_3: 0.4035 - dense_1_loss_4: 0.1065 - dense_1_loss_5: 0.0749 - dense_1_loss_6: 0.0505 - dense_1_loss_7: 0.0422 - dense_1_loss_8: 0.0364 - dense_1_loss_9: 0.0346 - dense_1_loss_10: 0.0296 - dense_1_loss_11: 0.0288 - dense_1_loss_12: 0.0295 - dense_1_loss_13: 0.0248 - dense_1_loss_14: 0.0258 - dense_1_loss_15: 0.0289 - dense_1_loss_16: 0.0286 - dense_1_loss_17: 0.0294 - dense_1_loss_18: 0.0275 - dense_1_loss_19: 0.0268 - dense_1_loss_20: 0.0299 - dense_1_loss_21: 0.0313 - dense_1_loss_22: 0.0279 - dense_1_loss_23: 0.0298 - dense_1_loss_24: 0.0301 - dense_1_loss_25: 0.0306 - dense_1_loss_26: 0.0300 - dense_1_loss_27: 0.0343 - dense_1_loss_28: 0.0339 - dense_1_loss_29: 0.0335 - 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 98/100
60/60 [==============================] - 0s - loss: 6.4238 - dense_1_loss_1: 3.7705 - dense_1_loss_2: 1.3386 - dense_1_loss_3: 0.3975 - dense_1_loss_4: 0.1044 - dense_1_loss_5: 0.0734 - dense_1_loss_6: 0.0495 - dense_1_loss_7: 0.0414 - dense_1_loss_8: 0.0356 - dense_1_loss_9: 0.0340 - dense_1_loss_10: 0.0290 - dense_1_loss_11: 0.0282 - dense_1_loss_12: 0.0289 - dense_1_loss_13: 0.0242 - dense_1_loss_14: 0.0253 - dense_1_loss_15: 0.0283 - dense_1_loss_16: 0.0280 - dense_1_loss_17: 0.0288 - dense_1_loss_18: 0.0270 - dense_1_loss_19: 0.0262 - dense_1_loss_20: 0.0292 - dense_1_loss_21: 0.0306 - dense_1_loss_22: 0.0274 - dense_1_loss_23: 0.0291 - dense_1_loss_24: 0.0295 - dense_1_loss_25: 0.0300 - dense_1_loss_26: 0.0294 - dense_1_loss_27: 0.0336 - dense_1_loss_28: 0.0332 - dense_1_loss_29: 0.0330 - 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 99/100
60/60 [==============================] - 0s - loss: 6.3855 - dense_1_loss_1: 3.7680 - dense_1_loss_2: 1.3272 - dense_1_loss_3: 0.3913 - dense_1_loss_4: 0.1026 - dense_1_loss_5: 0.0721 - dense_1_loss_6: 0.0486 - dense_1_loss_7: 0.0406 - dense_1_loss_8: 0.0350 - dense_1_loss_9: 0.0333 - dense_1_loss_10: 0.0284 - dense_1_loss_11: 0.0276 - dense_1_loss_12: 0.0283 - dense_1_loss_13: 0.0237 - dense_1_loss_14: 0.0248 - dense_1_loss_15: 0.0277 - dense_1_loss_16: 0.0274 - dense_1_loss_17: 0.0282 - dense_1_loss_18: 0.0264 - dense_1_loss_19: 0.0257 - dense_1_loss_20: 0.0286 - dense_1_loss_21: 0.0300 - dense_1_loss_22: 0.0268 - dense_1_loss_23: 0.0285 - dense_1_loss_24: 0.0290 - dense_1_loss_25: 0.0293 - dense_1_loss_26: 0.0288 - dense_1_loss_27: 0.0328 - dense_1_loss_28: 0.0325 - dense_1_loss_29: 0.0323 - 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 100/100
60/60 [==============================] - 0s - loss: 6.3481 - dense_1_loss_1: 3.7656 - dense_1_loss_2: 1.3165 - dense_1_loss_3: 0.3851 - dense_1_loss_4: 0.1007 - dense_1_loss_5: 0.0707 - dense_1_loss_6: 0.0476 - dense_1_loss_7: 0.0397 - dense_1_loss_8: 0.0343 - dense_1_loss_9: 0.0326 - dense_1_loss_10: 0.0279 - dense_1_loss_11: 0.0270 - dense_1_loss_12: 0.0277 - dense_1_loss_13: 0.0232 - dense_1_loss_14: 0.0243 - dense_1_loss_15: 0.0271 - dense_1_loss_16: 0.0269 - dense_1_loss_17: 0.0278 - dense_1_loss_18: 0.0258 - dense_1_loss_19: 0.0252 - dense_1_loss_20: 0.0280 - dense_1_loss_21: 0.0295 - dense_1_loss_22: 0.0262 - dense_1_loss_23: 0.0279 - dense_1_loss_24: 0.0284 - dense_1_loss_25: 0.0286 - dense_1_loss_26: 0.0282 - dense_1_loss_27: 0.0321 - dense_1_loss_28: 0.0319 - dense_1_loss_29: 0.0316 - 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     






    Out[10]:





<keras.callbacks.History at 0x7fcb9a0b06a0>

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.

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.]



In [11]:

    
# 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.



In [12]:

    
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.



In [13]:

    
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.



In [29]:

    
# 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
    pred = np.array(pred)
    # print(pred.shape)
    indices = np.argmax(pred, axis=2)
    # Step 3: Convert indices to one-hot vectors, the shape of the results should be (1, )
    results = to_categorical(indices, num_classes=78)
    ### END CODE HERE ###
    
    return results, indices



In [25]:

    
results, indices = predict_and_sample(inference_model, x_initializer, a_initializer, c_initializer)

# 
#print('indices' + str(indices.shape))
#print(results.shape)


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]))









    



(50, 1, 78)
np.argmax(results[12]) = 39
np.argmax(results[17]) = 13
list(indices[12:18]) = [array([39]), array([57]), array([64]), array([31]), array([69]), array([13])]



In [26]:

    
print(results.shape)



In [27]:

    
print('indices' + str(indices.shape))









    



indices(50, 1)

Tips for the cell above:

First convert the pred to an numpy array using np.array and then use np.argmax to get the indices. Also, please look at the shape after you do np.array to infer the correct value to pass to the axis parameter.

pred = np.array(pred)

then

print(pred.shape)

will give (50, 1, 78)

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.



In [28]:

    
out_stream = generate_music(inference_model)









    



Predicting new values for different set of chords.
Generated 50 sounds using the predicted values for the set of chords ("1") and after pruning
Generated 51 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 50 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.



In [17]:

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









    Out[17]:

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])]