運(yùn)行環(huán)境?

TensorFlow2.0

解決方案

from tensorflow import keras import tensorflow as tf import mnist_reader import numpy as np import matplotlib.pyplot as plt(train_images, train_labels), (test_images, test_labels) = mnist_reader.load_data('../data/mnist') class_names = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '0']train_images = train_images / 255.0 test_images = test_images / 255.0plt.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='relu'),keras.layers.Dense(10) ]) model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy']) model.fit(train_images, train_labels, epochs=10) test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)print('\nTest accuracy:', test_acc) probability_model = tf.keras.Sequential([model,tf.keras.layers.Softmax()]) predictions = probability_model.predict(test_images) print(predictions[0]) print(np.argmax(predictions[0])) print(test_labels[0])def plot_image(i, predictions_array, true_label, img):predictions_array, true_label, img = predictions_array, 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],100 * np.max(predictions_array),class_names[true_label]),color=color)def plot_value_array(i, predictions_array, true_label):predictions_array, true_label = predictions_array, true_label[i]plt.grid(False)plt.xticks(range(10))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 = 0 plt.figure(figsize=(6, 3)) plt.subplot(1, 2, 1) plot_image(i, predictions[i], test_labels, test_images) plt.subplot(1, 2, 2) plot_value_array(i, predictions[i], test_labels) plt.show()i = 12 plt.figure(figsize=(6, 3)) plt.subplot(1, 2, 1) plot_image(i, predictions[i], test_labels, test_images) plt.subplot(1, 2, 2) plot_value_array(i, predictions[i], test_labels) plt.show()# Plot the first X test images, their predicted labels, and the true labels. # Color correct predictions in blue and incorrect predictions in red. num_rows = 5 num_cols = 3 num_images = num_rows * num_cols plt.figure(figsize=(2 * 2 * num_cols, 2 * num_rows)) for i in range(num_images):plt.subplot(num_rows, 2 * num_cols, 2 * i + 1)plot_image(i, predictions[i], test_labels, test_images)plt.subplot(num_rows, 2 * num_cols, 2 * i + 2)plot_value_array(i, predictions[i], test_labels) plt.tight_layout() plt.show()# Grab an image from the test dataset. img = test_images[1]print(img.shape)# Add the image to a batch where it's the only member. img = (np.expand_dims(img, 0))print(img.shape)predictions_single = probability_model.predict(img)print(predictions_single)plot_value_array(1, predictions_single[0], test_labels) _ = plt.xticks(range(10), class_names, rotation=45)print(np.argmax(predictions_single[0]))

參考文章

TensorFlow 教程——基本分類：對服裝圖像進(jìn)行分類

深度學(xué)習(xí)原理與 TensorFlow實(shí)踐

總結(jié)

以上是生活随笔為你收集整理的TensorFlow 教程——手写数字识别的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯，歡迎將生活随笔推薦給好友。