你的第一个神经网络

"""
对服装图像进行分类
"""
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt

# print(tf.__version__)

#读取数据   ##经典mmist数据集
fashion_mnist = keras.datasets.fashion_mnist

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


# #认识训练集的数据
# print(train_images.shape)
# #训练集中的标签个数
# print(len(train_labels))
# #标签
# print(train_labels)
# # 测试集中图像个数
# print(test_images.shape)
# # 测试集的个数
# print(len(test_labels))

# 预处理数据
# plt.figure()
# plt.imshow(train_images[0])
# plt.colorbar()
# plt.grid(False)
# plt.show()

#将这些值缩小到0-1之间，然后将其送到神经网络
train_images = train_images / 255.0
test_images = test_images / 255.0

# 验证数据格式是否正确，，，，显示前25个图象
# 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()

#构建模型
# 1.设置层
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28,28)),
    keras.layers.Dense(128,activation='relu'),
    keras.layers.Dense(10)
])

#编译模型
model.compile(optimizer='adam',#优化器
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),#损失函数
              metrics=['accuracy'])#正确率
# 训练模型
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)

# 进行预测
#logits
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)
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')
#验证预测结果
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()

# 使用训练好的模型
# 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]))