1.4 深度神经网络 与 卷积神经网络分别实现图像识别 （TensorFlow ）

import tensorflow as tf
# 深度神经网络
mnist = tf.keras.datasets.fashion_mnist
(training_images,training_labels),(test_images,test_labels) = mnist.load_data()
training_images = training_images/255.0
test_images = test_images/255.0
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128,activation=tf.nn.relu),
    tf.keras.layers.Dense(10,activation=tf.nn.softmax)
])
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
model.fit(training_images,training_labels,epochs=5)
test_loss = model.evaluate(test_images,test_labels)

# 卷积神经网络
import tensorflow as tf
print(tf.version)
minist = tf.keras.datasets.fashion_mnist
(training_images,training_labels),(test_images,test_labels) = mnist.load_data()
training_images = training_images.reshape(60000,28,28,1)
training_images = training_images/255.0
test_images = test_images.reshape(10000,28,28,1)
test_images = test_images/255.0
model = tf.keras.models.Sequential([
    # 过滤器 64个3*3，输入图形28*28*1，1为色彩维度
    tf.keras.layers.Conv2D(64,(3,3),activation='relu',input_shape=(28,28,1)),
    #最大池化2*2
    tf.keras.layers.MaxPool2D(2,2),
    tf.keras.layers.Conv2D(64,(3,3),activation='relu'),
    tf.keras.layers.MaxPool2D(2,2),
    # 建立神经元
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128,activation='relu'),
    tf.keras.layers.Dense(10,activation='softmax')
])
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
model.summary()
model.fit(training_images,training_labels,epochs=5)
test_loss = model.evaluate(test_images,test_labels)