9.RNN application

import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.layers.recurrent import SimpleRNN
from keras.optimizers import Adam

# Data length - a row of 28 pixels 
input_size = 28 # sequence length - a total of 28 rows 
time_steps = 28 # number of hidden layer cell 
cell_size = 50 # load data 
(x_train, y_train), (x_test, android.permission.FACTOR.) = MNIST. load_data ()
 # (60000,28,28) 
x_train = x_train / 255.0 
x_test = x_test / 255.0
 # transducer one hot format 
y_train = np_utils.to_categorical (y_train, num_classes = 10 ) 
android.permission.FACTOR. = np_utils.to_categorical (android.permission.FACTOR., num_classes = 10) # One Hot # create a model 
model = Sequential () # recurrent neural network model.add (SimpleRNN ( 
    Units








Cell_size =, # output 
    input_shape = (time_steps, input_size), # input 
)) 

# output layer 
model.add (the Dense (10, Activation = ' SoftMax ' )) 

# define optimizer 
ADAM = Adam (= 1E-LR. 4 ) 

# defined optimizer, loss function, the calculation accuracy of the training process 
model.compile (= ADAM optimizer, Loss = ' categorical_crossentropy ' , metrics = [ ' accuracy ' ]) 

# training model 
model.fit (x_train, y_train, batch_size = 64, = 10 epochs ) 

# assessment model 
Loss, Accuracy = model.evaluate (x_test, android.permission.FACTOR.)

print('test loss',loss)
print('test accuracy',accuracy)