TensorFlow——基于Keras子类API的fashion-mnist数据集图像分类

太过爱你忘了你带给我的痛 2023-01-11 01:19 143阅读 0赞

[https://tensorflow.google.cn/tutorials/keras/classification][https_tensorflow.google.cn_tutorials_keras_classification]

# 解决方案  #

#!usr/bin/env python
    # -*- coding:utf-8 _*-
    """
    @version: 0.0.1
    author: ShenTuZhiGang
    @time: 2021/01/25 16:33
    @file: 12.py
    @function:
    @modify:
    """
    
    from tensorflow import keras
    import tensorflow as tf
    import mnist_reader
    import numpy as np
    import matplotlib.pyplot as plt
    from tensorflow import summary
    import datetime
    
    
    current_time = str(datetime.datetime.now().timestamp())
    train_log_dir = '/content/drive/My Drive/colab notebooks/output/tsboardx/train/' + current_time
    test_log_dir = '/content/drive/My Drive/colab notebooks/output/tsboardx/test/' + current_time
    val_log_dir = '/content/drive/My Drive/colab notebooks/output/tsboardx/val/' + current_time
    train_summary_writer = summary.create_file_writer(train_log_dir)
    val_summary_writer = summary.create_file_writer(val_log_dir)
    test_summary_writer = summary.create_file_writer(test_log_dir)
    (train_images, train_labels), (test_images, test_labels) = mnist_reader.load_data('../data/fashion')
    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
    
    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()
    
    class FashionMnistModel(keras.Model):
        def __init__(self, **kwargs):
            super().__init__(**kwargs)
            self.input_ = keras.layers.Flatten(input_shape=[28, 28])
            self.hidden1 = keras.layers.Dense(128, activation="relu")
            self.main_output = keras.layers.Dense(10)
    
        def call(self, inputs, **kwargs):
            input_a = self.input_(inputs)
            hidden1 = self.hidden1(input_a)
            output = self.main_output(hidden1)
            return output
    
    
    model = FashionMnistModel()
    model.build(input_shape=(0, 28, 28))
    model.summary()
    model.compile(optimizer='adam',
                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                  metrics=['accuracy'])
    history = model.fit(train_images, train_labels, epochs=10)
    test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
    with test_summary_writer.as_default():
        summary.scalar('loss', test_loss, 10)
        summary.scalar('accuracy', test_acc, 10)
    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]))

# 参考文章 #

[TensorFlow——本地加载fashion-mnist数据集][TensorFlow_fashion-mnist]

[TensorFlow 教程——基本分类：对服装图像进行分类][TensorFlow]

[https_tensorflow.google.cn_tutorials_keras_classification]: https://tensorflow.google.cn/tutorials/keras/classification
[TensorFlow_fashion-mnist]: https://shentuzhigang.blog.csdn.net/article/details/110006990
[TensorFlow]: https://shentuzhigang.blog.csdn.net/article/details/110008487