Train your first neural network: basic classification（tensorflow官网你的第一个神经网络）

#导入需要的模块

import tensorflow as tf
from tensorflow import keras

import numpy as np
import matplotlib.pyplot as plt

print(tf.__version__)

#从网络（自动从网络下载）或者本地读取数据集

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

#将图片的标签由数字映射到字符串数组中

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

#检查图片和标签的大小

train_images.shape
len(train_labels)
train_labels

test_images.shape
len(test_labels)

test_labels

#检查数据

plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)

#图像归一化 由整数变为浮点数

train_images = train_images/255
test_images  = test_images/255

#显示前25张图片检查数据

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_images[i],cmap=plt.cm.binary)
        plt.xlabel(class_names[train_labels[i]])

#构建模型

model = keras.Sequential([
        keras.layers.Flatten(input_shape=(28,28)),       #图像由二维数组变为一维数组  （28*28） （28*28=784）
        keras.layers.Dense(128,activation=tf.nn.relu),   #128个神经元节点的dense层
        keras.layers.Dense(10,activation=tf.nn.softmax)  #10个输出节点的softmax层
        ])

#编译模型

model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy']
                  )

#训练模型

model.fit(train_images, train_labels, epochs=5)       #训练5次

#评估准确性

test_loss,  test_acc = model.evaluate(test_images,test_labels)
print('test accuracy',test_acc)

#做出预测（输出是一个10个数字的数组，描述模型的“置信度”）

predictions = model.predict(test_images)

#对第一个图像预测

predictions[0]
np.argmax(predictions[0])
test_labels[0]

#定义两个函数

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')

#第一个图像、预测数、预测数组

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)

#第二个突袭、预测数、预测数组
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)

绘制第一个X测试图像、它们的预测标签和真实标签
蓝色表示正确预测，红色表示错误预测

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)

#从测试数据集中获取图像

img = test_images[0]

print(img.shape)

#将图像添加到一个批处理中，其中它是唯一的成员。

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)

    p.argmax(predictions_single[0])