源码：

import tensorflow as tffrom tensorflow
import keras
import numpy as np
import matplotlib.pyplot as plt
print(tf.version)
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.shape
len(train_labels)
train_labels
test_images.shape
len(test_labels)
plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()
train_images = train_images / 255.0
test_images = test_images / 255.0
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()
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=‘adam’,
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)
predictions = model.predict(test_images)
predictions[0]
np.argmax(predictions[0])
test_labels[0]
def plot_image(i, predictions_array, true_label, img): predictions_array, true_label, img = predictions_array[i], true_label[i], img[i] plt.grid(False) plt.xticks([]) plt.yticks([])
plt.imshow(img, cmap=plt.cm.binary)
predicted_label = np.argmax(predictions_array)
if predicted_label == true_label:
color = ‘blue’
else:
color = ‘red’
plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
100np.max(predictions_array),
class_names[true_label]),
color=color)
def plot_value_array(i, predictions_array, true_label):
predictions_array, true_label = predictions_array[i], true_label[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
thisplot = plt.bar(range(10), predictions_array, color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array)
thisplot[predicted_label].set_color(‘red’)
thisplot[true_label].set_color(‘blue’)
i = 12
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions, test_labels)
plt.show()
num_rows = 5
num_cols = 3
num_images = num_rowsnum_cols
plt.figure(figsize=(22num_cols, 2num_rows))
for i in range(num_images):
plt.subplot(num_rows, 2num_cols, 2i+1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(num_rows, 2num_cols, 2*i+2)
plot_value_array(i, predictions, test_labels)
plt.show()

img = test_images[0]
print(img.shape)

img = (np.expand_dims(img,0))
print(img.shape)
predictions_single = model.predict(img)
print(predictions_single)
plot_value_array(0, predictions_single, test_labels)
plt.xticks(range(10), class_names, rotation=45)
plt.show()
prediction_result = np.argmax(predictions_single[0])
print(prediction_result)