Deep Learning: Keras + MNIST

8015 단어 deeplearning
Keras + MNIST
  • Data representations for neural networks

  • Data representations for neural networks
    # -*- coding: utf-8 -*-
    
    """
    @Date: 2018/9/28
    
    @Author: dreamhomes
    
    @Summary:
    """
    
    from keras.utils import to_categorical
    from keras import models
    from keras import layers
    
    import numpy as np
    
    
    # (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
    path = "./mnist.npz"
    f = np.load(path)
    # N=60000
    train_images, train_labels = f['x_train'], f['y_train']
    print(len(train_labels))
    
    #import matplotlib.pyplot as plt
    # digit = train_images[0]
    # plt.imshow(digit, cmap=plt.cm.binary)
    # plt.show()
    
    
    test_images, test_labels = f['x_test'], f['y_test']
    
    network = models.Sequential()
    network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,)))
    network.add(layers.Dense(512, activation='relu'))
    network.add(layers.Dense(10, activation='softmax'))
    
    network.compile(optimizer='rmsprop',
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])
    
    train_images = train_images.reshape((60000, 28 * 28))
    # print(train_images[1])
    
    train_images = train_images.astype('float32') / 255
    
    test_images = test_images.reshape((10000, 28 * 28))
    test_images = test_images.astype('float32') / 255
    
    train_labels = to_categorical(train_labels)
    test_labels = to_categorical(test_labels)
    
    history = network.fit(train_images, train_labels, epochs=5, batch_size=128)
    
    # print(history.history.keys())
    
    test_loss, test_acc = network.evaluate(test_images, test_labels)
    
    print('test_acc:', test_acc)
    
    

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