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python:pypi:tensorflow

TensorFlow

import tensorflow as tf
from tensorflow import keras
 
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
 
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images = train_images / 255.0
test_images = test_images / 255.0
 
 
def draw():
    import numpy as np
    import matplotlib.pyplot as plt
 
    plt.figure()
    plt.imshow(train_images[0])
    plt.colorbar()
    plt.grid(False)
 
    plt.figure(figsize=(10, 10))
    for i in range(25):
        plt.subplot(5, 5, i+1)
        plt.xticks([])
        plt.yticks([])
        plt.grid(False)
        plt.imshow(train_images[i], cmap=plt.cm.binary)
        plt.xlabel(class_names[train_labels[i]])
    plt.show()
 
 
def train():
    _model = keras.Sequential([
        keras.layers.Flatten(input_shape=(28, 28)),
        keras.layers.Dense(128, activation=tf.nn.relu),
        keras.layers.Dense(10, activation=tf.nn.softmax)
    ])
    _model.compile(optimizer=tf.train.AdamOptimizer(),
                   loss='sparse_categorical_crossentropy',
                   metrics=['accuracy'])
    _model.fit(train_images, train_labels, epochs=5)
    test_loss, test_acc = _model.evaluate(test_images, test_labels)
    print('Test accuracy:', test_acc)
    return _model
 
 
if __name__ == "__main__":
    draw()
    model = train()

参考

python/pypi/tensorflow.txt · 最后更改: 2019/08/27 14:38 由 老赵