1.日常import
from __future__ import absolute_import, division, print_function import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers import numpy as np import matplotlib.pyplot as plt import os #调用GPU加速训练 os.environ['CUDA_VISIBLE_DEVICES'] = '/gpu:1'
2.数据集加载
#查看tf版本 print(tf.__version__) #加载数据集 (train_images, train_labels), (test_images, test_labels) = keras.datasets.fashion_mnist.load_data()
3.数据集标签及数据集可视化
#data label class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] #print label print(train_images.shape) print(train_labels.shape) print(test_images.shape) print(test_labels.shape) #显示数据 plt.figure() plt.imshow(train_images[1]) plt.colorbar() plt.grid(False) plt.show() #data per train_images = train_images / 255.0 test_images = test_images / 255.0 #display part data 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()
结果如下
4.模型搭建并训练测试
# setup model model = keras.Sequential( [ layers.Flatten(input_shape=[28, 28]), layers.Dense(128, activation='relu'), layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(train_images, train_labels, epochs=5) model.evaluate(test_images, test_labels) predictions = 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[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], 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[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 = 0 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() # Plot the first X test images, their predicted label, and the true label # Color correct predictions in blue, 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, test_labels, test_images) plt.subplot(num_rows, 2*num_cols, 2*i+2) plot_value_array(i, predictions, test_labels) plt.show() img = test_images[0] 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)
测试结果如下
5.完整代码
from __future__ import absolute_import, division, print_function import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers import numpy as np import matplotlib.pyplot as plt import os os.environ['CUDA_VISIBLE_DEVICES'] = '/gpu:1' print(tf.__version__) (train_images, train_labels), (test_images, test_labels) = keras.datasets.fashion_mnist.load_data() #data label class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] #print label print(train_images.shape) print(train_labels.shape) print(test_images.shape) print(test_labels.shape) #显示数据 plt.figure() plt.imshow(train_images[1]) plt.colorbar() plt.grid(False) plt.show() #data per train_images = train_images / 255.0 test_images = test_images / 255.0 #display part data 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() # setup model model = keras.Sequential( [ layers.Flatten(input_shape=[28, 28]), layers.Dense(128, activation='relu'), layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(train_images, train_labels, epochs=5) model.evaluate(test_images, test_labels) predictions = 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[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], 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[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 = 0 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() # Plot the first X test images, their predicted label, and the true label # Color correct predictions in blue, 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, test_labels, test_images) plt.subplot(num_rows, 2*num_cols, 2*i+2) plot_value_array(i, predictions, test_labels) plt.show() img = test_images[0] 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)