Create MNIST.py

keras/convolutional_neural_networks/MNIST.py

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+from __future__ import print_function
+from bigdl.dataset import mnist
+from bigdl.util.common import *
+import numpy as np
+from bigdl.nn.keras.topology import Sequential
+from bigdl.nn.keras.layer import *
+from bigdl.nn.criterion import *
+
+
+mnist_path = "datasets/mnist"
+(X_train, Y_train), (X_test, Y_test) = mnist.load_data(mnist_path)
+X_train=X_train[0:20000]
+Y_train=Y_train[0:20000]
+
+X_test=X_test[0:2000]
+Y_test=Y_test[0:2000]
+
+print(X_train.shape)
+print(X_test.shape)
+print(Y_train.shape)
+print(Y_test.shape)
+
+num_fc = 512
+num_outputs = 10
+model = Sequential()
+model.add(Reshape((1, 28, 28), input_shape=(28, 28, 1)))
+model.add(Convolution2D(20, 3, 3, activation="relu", input_shape=(1, 28, 28)))
+model.add(MaxPooling2D())
+model.add(Convolution2D(50, 3, 3, activation="relu", name="conv2_5x5"))
+model.add(MaxPooling2D())
+model.add(Flatten())
+model.add(Dense(num_fc, activation="relu", name="fc1"))
+model.add(Dense(num_outputs, activation="softmax", name="fc2"))
+
+print(model.get_input_shape())
+print(model.get_output_shape())
+
+model.compile(loss='sparse_categorical_crossentropy',
+                  optimizer='adam',
+                metrics=['accuracy'])
+
+model.fit(X_train, Y_train, batch_size=32, nb_epoch=2,validation_data=(X_test, Y_test))