<gh_stars>0
import matplotlib.pyplot as plt
import tensorflow as tf
import keras

# Place tensors on the CPU
with tf.device('/CPU:0'):
 tf.debugging.set_log_device_placement(True)
 (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
 image_index = 7777 # You may select anything up to 60,000
 print(y_train[image_index]) # The label is 8
 plt.imshow(x_train[image_index], cmap='Greys')
 
 # Reshaping the array to 4-dims so that it can work with the Keras API
 x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
 x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
 input_shape = (28, 28, 1)
 # Making sure that the values are float so that we can get decimal points after division
 x_train = x_train.astype('float32')
 x_test = x_test.astype('float32')
 # Normalizing the RGB codes by dividing it to the max RGB value.
 x_train /= 255
 x_test /= 255
 print('x_train shape:', x_train.shape)
 print('Number of images in x_train', x_train.shape[0])
 print('Number of images in x_test', x_test.shape[0])
 
 
 # Importing the required Keras modules containing model and layers
 from keras.models import Sequential
 from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D
 # Creating a Sequential Model and adding the layers
 model = Sequential()
 model.add(Conv2D(28, kernel_size=(3,3), input_shape=input_shape))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Flatten()) # Flattening the 2D arrays for fully connected layers
 model.add(Dense(128, activation=tf.nn.relu))
 model.add(Dropout(0.2))
 model.add(Dense(10,activation=tf.nn.softmax))
 
 
 model.compile(optimizer='adam', 
               loss='sparse_categorical_crossentropy', 
               metrics=['accuracy'])
 model.fit(x=x_train,y=y_train, epochs=10)
 
 # serialize model to JSON
 model_json = model.to_json()
 with open("model.json", "w") as json_file:
     json_file.write(model_json)
 # serialize weights to HDF5
 model.save_weights("model.h5")
 print("Saved model to disk")
 

import matplotlib.pyplot as plt
import tensorflow as tf
import keras

# Place tensors on the CPU
with tf.device('/CPU:0'):
 tf.debugging.set_log_device_placement(True)
 (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
 image_index = 7777 # You may select anything up to 60,000
 print(y_train[image_index]) # The label is 8
 plt.imshow(x_train[image_index], cmap='Greys')
 
 # Reshaping the array to 4-dims so that it can work with the Keras API
 x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
 x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
 input_shape = (28, 28, 1)
 # Making sure that the values are float so that we can get decimal points after division
 x_train = x_train.astype('float32')
 x_test = x_test.astype('float32')
 # Normalizing the RGB codes by dividing it to the max RGB value.
 x_train /= 255
 x_test /= 255
 print('x_train shape:', x_train.shape)
 print('Number of images in x_train', x_train.shape[0])
 print('Number of images in x_test', x_test.shape[0])
 
 
 # Importing the required Keras modules containing model and layers
 from keras.models import Sequential
 from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D
 # Creating a Sequential Model and adding the layers
 model = Sequential()
 model.add(Conv2D(28, kernel_size=(3,3), input_shape=input_shape))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Flatten()) # Flattening the 2D arrays for fully connected layers
 model.add(Dense(128, activation=tf.nn.relu))
 model.add(Dropout(0.2))
 model.add(Dense(10,activation=tf.nn.softmax))
 
 
 model.compile(optimizer='adam', 
               loss='sparse_categorical_crossentropy', 
               metrics=['accuracy'])
 model.fit(x=x_train,y=y_train, epochs=10)
 
 # serialize model to JSON
 model_json = model.to_json()
 with open("model.json", "w") as json_file:
     json_file.write(model_json)
 # serialize weights to HDF5
 model.save_weights("model.h5")
 print("Saved model to disk")
# Place tensors on the CPU # You may select anything up to 60,000 # The label is 8 # Reshaping the array to 4-dims so that it can work with the Keras API # Making sure that the values are float so that we can get decimal points after division # Normalizing the RGB codes by dividing it to the max RGB value. # Importing the required Keras modules containing model and layers # Creating a Sequential Model and adding the layers # Flattening the 2D arrays for fully connected layers # serialize model to JSON # serialize weights to HDF5