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- import tensorflow as tf
- from keras.models import Sequential
- from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D
- sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
- (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
- 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])
- 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=100)
- model.evaluate(x_test, y_test)
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