TensorFlow图像分类

参考文章：https://zhuanlan.zhihu.com/p/59506238

import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

(train_image,train_label),(test_image,test_label) = tf.keras.datasets.fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_image = train_image/255
test_image = test_image/255
#显示数据
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_image[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_label[i]])
plt.show()
#构建网络
model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28)))
model.add(tf.keras.layers.Dense(128,activation='relu'))
model.add(tf.keras.layers.Dense(10,activation='softmax'))
#预测
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['acc'])
model.fit(train_image,train_label,epochs=5)
print(model.evaluate(test_image,test_label))
predict = model.predict(test_image)
print(np.argmax(predict[0]))
print(test_label[0])
#显示单张图片
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)
#显示数组全部图片
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, predict, test_label, test_image)
plt.subplot(1,2,2)
plot_value_array(i, predict,  test_label)
plt.show()
#可视化结果
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, predict, test_label, test_image)
  plt.subplot(num_rows, 2*num_cols, 2*i+2)
  plot_value_array(i, predict, test_label)
plt.show()

img = test_image[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_label)
_ = plt.xticks(range(10), class_names, rotation=45)
(1, 28, 28)