74 lines
2.1 KiB
Python
Executable File
74 lines
2.1 KiB
Python
Executable File
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import numpy as np
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import time
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from keras.datasets import mnist
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from keras.models import Sequential
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from keras.layers.core import Dense, Flatten, Dropout, Activation
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from keras.utils import np_utils
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import matplotlib.pyplot as plt
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(X_train, y_train), (X_test, y_test) = mnist.load_data()
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num_pixels = X_train.shape[1] * X_train.shape[2]
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num_classes = 10
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fig = plt.figure()
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for i in range(9):
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plt.subplot(3,3,i+1)
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plt.tight_layout()
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plt.imshow(X_test[i], cmap='gray', interpolation='none')
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plt.title("Digit: %d" % (y_test[i]))
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plt.xticks([])
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plt.yticks([])
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fig.show()
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t1 = time.time()
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X_train = X_train.reshape(60000, 784) / 255
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X_test = X_test.reshape(10000, 784) / 255
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X_train = X_train.astype('float32')
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X_test = X_test.astype('float32')
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# let's print the shape before we reshape and normalize
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print("X_train shape", X_train.shape)
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print("y_train shape", y_train.shape)
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print("X_test shape", X_test.shape)
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print("y_test shape", y_test.shape)
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Y_train = np_utils.to_categorical(y_train, num_classes)
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Y_test = np_utils.to_categorical(y_test, num_classes)
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t2 = time.time()
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print("Preprocessing took %.2f sec." % (t2 - t1))
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t1 = time.time()
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# building a linear stack of layers with the sequential model
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model = Sequential()
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model.add(Dense(512, input_shape = (784,)))
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model.add(Activation('relu'))
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model.add(Dropout(0.2))
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model.add(Dense(512))
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model.add(Activation('relu'))
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model.add(Dropout(0.2))
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model.add(Dense(10))
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model.add(Activation('softmax'))
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print("Training network ...")
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model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
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print("Fitting data")
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model.fit(X_train, Y_train, batch_size=128, epochs=15,
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verbose=1, validation_data=(X_test, Y_test))
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t2 = time.time()
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print("Training took %.2f sec." % (t2 - t1))
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print("Making predictions...")
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t1 = time.time()
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for i in range(9):
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img = np.array(X_test[i][np.newaxis,:])
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preds = model.predict_classes(img)
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print("Image[", i, "] - Me thinks me saw a : ", int(preds[0]) )
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t2 = time.time()
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print("Predictions took %.2f sec." % (t2 - t1))
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