Wu Yuxiong - born natural neural networks and deep learning practical Python + Keras + TensorFlow: Getting convolution neural network

from keras import layers
from keras import models

Model = models.Sequential ()
 # ground floor receives a two-dimensional input 
model.add (layers.Conv2D (32, (3,3), Activation = ' RELU ' , = input_shape (28,28,1 )))
model.add(layers.MaxPooling2D(2,2))
model.add(layers.Conv2D(64, (3,3), activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64, (3,3), activation='relu'))

model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()

 

 

from keras.datasets import mnist
from keras.utils import to_categorical

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255

test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_images, train_labels, epochs = 5, batch_size=64)

test_loss, test_acc = model.evaluate(test_images, test_labels)
print(test_acc)