TensorFlow：基本分类算法的基本流程

# encoding: utf-8

# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt


print(tf.__version__)


fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']


print(train_images.shape)
print(len(train_labels))
print(test_images.shape)
print(len(test_labels))


plt.figure()
"""
matplotlib.pyplot.imshow(X, cmap=None)
cmap: 颜色图谱（colormap), 默认绘制为RGB(A)颜色空间。
"""
plt.imshow(train_images[0])
plt.colorbar()
"""
plt.grid(True)  显示背景的网格线 
plt.grid(False)  关闭背景的网格线 
ax.grid(color=’r’, linestyle=’-‘, linewidth=2)  设置背景网格线的样式
"""
plt.grid(False)
plt.savefig("1.png")


train_images = train_images / 255.0
test_images = test_images / 255.0


plt.figure(figsize=(10,10))
for i in range(25):
    """
    subplot(nrows, ncols, index, **kwargs)
    """
    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.savefig("2.png")


model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation=tf.nn.relu),
    keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer=tf.train.AdamOptimizer(), 
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5)
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test accuracy:', test_acc)
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")
  """
  matplotlib.pyplot.ylim(*args, **kwargs)  设置坐标轴范围
  """
  plt.ylim((0, 1))
  predicted_label = np.argmax(predictions_array)
  """
  预测结果对应标签的颜色为红色
  真实标签对应的颜色为蓝色
  """
  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.savefig("3.png")

i = 12
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.savefig("4.png")

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.savefig("5.png")


"""
预测单张图片
"""
img = test_images[0]
print(img.shape)
"""
把图片添加到一个 batch 中（这个 batch 仅包含这一张图片）
"""
img = (np.expand_dims(img,0))
print(img.shape)
predictions_single = model.predict(img)
print(predictions_single)
print(np.argmax(predictions_single[0]))

运行结果（图片）

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