tensorflow 实现低分辨率灰度图像分类算法

# TensorFlow and tf.kerasimport tensorflow as tffrom tensorflow import keras
# Helper librariesimport numpy as npimport matplotlib.pyplot as pltfashion_mnist = keras.datasets.fashion_mnist(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
#图像是 28x28 的 NumPy 数组，像素值介于 0 到 255 之间。标签是整数数组，介于 0 到 9 之间。这些标签对应于图像所代表的服装类：# 标签    类# 0 T恤/上衣# 1 裤子# 2 套头衫# 3 连衣裙# 4 外套# 5 凉鞋# 6 衬衫# 7 运动鞋# 8 包# 9 短靴class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']#在训练网络之前，必须对数据进行预处理。如果您检查训练集中的第一个图像，您会看到像素值处于 0 到 255 之间：plt.figure()plt.imshow(train_images[0])plt.colorbar()plt.grid(False)plt.show()#将这些值缩小至 0 到 1 之间，然后将其馈送到神经网络模型。为此，请将这些值除以 255。请务必以相同的方式对训练集和测试集进行预处理：train_images = train_images / 255.0test_images = test_images / 255.0#为了验证数据的格式是否正确，以及您是否已准备好构建和训练网络，让我们显示训练集中的前 25 个图像，并在每个图像下方显示类名称。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()#构建神经网络需要先配置模型的层，然后再编译模型。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'])#要开始训练，请调用 model.fit 方法，这样命名是因为该方法会将模型与训练数据进行“拟合”：model.fit(train_images, train_labels, epochs=10)#评估准确率test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)print('\nTest accuracy:', test_acc)#在模型经过训练后，您可以使用它对一些图像进行预测。probability_model = tf.keras.Sequential([model,                                          tf.keras.layers.Softmax()])predictions = probability_model.predict(test_images)#可以将其绘制成图表，看看模型对于全部 10 个类的预测。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')#我们来看看第 0 个图像、预测结果和预测数组。正确的预测标签为蓝色，错误的预测标签为红色。数字表示预测标签的百分比（总计为 100）。i = 0plt.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()#让我们用模型的预测绘制几张图像。请注意，即使置信度很高，模型也可能出错。num_rows = 5
num_cols = 3num_images = num_rows*num_colsplt.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()
# MIT License## Copyright (c) 2017 François Chollet## Permission is hereby granted, free of charge, to any person obtaining a# copy of this software and associated documentation files (the "Software"),# to deal in the Software without restriction, including without limitation# the rights to use, copy, modify, merge, publish, distribute, sublicense,# and/or sell copies of the Software, and to permit persons to whom the# Software is furnished to do so, subject to the following conditions:## The above copyright notice and this permission notice shall be included in# all copies or substantial portions of the Software.## THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER# DEALINGS IN THE SOFTWARE.

1875/1875 [==============================] - 5s 3ms/step - loss: 0.4947 - accuracy: 0.8247Epoch 2/101875/1875 [==============================] - 5s 3ms/step - loss: 0.3696 - accuracy: 0.8665Epoch 3/101875/1875 [==============================] - 6s 3ms/step - loss: 0.3331 - accuracy: 0.8787Epoch 4/101875/1875 [==============================] - 6s 3ms/step - loss: 0.3118 - accuracy: 0.8868Epoch 5/101875/1875 [==============================] - 6s 3ms/step - loss: 0.2939 - accuracy: 0.8910Epoch 6/101875/1875 [==============================] - 6s 3ms/step - loss: 0.2805 - accuracy: 0.8954Epoch 7/101875/1875 [==============================] - 5s 3ms/step - loss: 0.2668 - accuracy: 0.9017Epoch 8/101875/1875 [==============================] - 6s 3ms/step - loss: 0.2551 - accuracy: 0.9050Epoch 9/101875/1875 [==============================] - 6s 3ms/step - loss: 0.2471 - accuracy: 0.9079Epoch 10/101875/1875 [==============================] - 6s 3ms/step - loss: 0.2388 - accuracy: 0.9098313/313 - 0s - loss: 0.3377 - accuracy: 0.8837
Test accuracy: 0.8837000131607056