tebsorflow2.0 深度学习基础和tf.keras（下）

2022-10-18 90

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简介： 1. tf.keras实现线性回归(1)1.1 Income数据导入可视化

3.2 数据准备

import tensorflow as tf
print('Tensorflow Version: {}'.format(tf.__version__))
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
#图像，标签，测试图像与标签
(train_image, train_lable), (test_image, test_label) = tf.keras.datasets.fashion_mnist.load_data()
print("train_image shappe:{}\ntest_image shape:{}".format(train_image.shape,test_image.shape))
plt.imshow(train_image[0])
plt.axis("off")
plt.show()
#归一化
train_image = train_image/255
test_image = test_image/255

Tensorflow Version: 2.0.0
train_image shappe:(60000, 28, 28)
test_image shape:(10000, 28, 28)

3.3 建立模型并训练

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"))
model.summary()
model.compile(optimizer = "adam",loss='sparse_categorical_crossentropy',metrics=['acc'])
model.fit(train_image,train_lable,epochs=5)

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_2 (Flatten)          (None, 784)               0         
_________________________________________________________________
dense_4 (Dense)              (None, 128)               100480    
_________________________________________________________________
dense_5 (Dense)              (None, 10)                1290      
=================================================================
Total params: 101,770
Trainable params: 101,770
Non-trainable params: 0
_________________________________________________________________
Train on 60000 samples
Epoch 1/5
60000/60000 [==============================] - 8s 138us/sample - loss: 3.1367 - acc: 0.6865
Epoch 2/5
60000/60000 [==============================] - 8s 130us/sample - loss: 0.6911 - acc: 0.7243
Epoch 3/5
60000/60000 [==============================] - 7s 114us/sample - loss: 0.6168 - acc: 0.7527
Epoch 4/5
60000/60000 [==============================] - 8s 128us/sample - loss: 0.5736 - acc: 0.7814
Epoch 5/5
60000/60000 [==============================] - 7s 117us/sample - loss: 0.5319 - acc: 0.8114

#评价
model.evaluate(test_image, test_label)

10000/1  - 1s 69us/sample - loss: 0.3796 - acc: 0.8244
[0.5249296184539795, 0.8244]

3.5 采用one-hot编码方式训练

train_label_onehot = tf.keras.utils.to_categorical(train_lable)
test_label_onehot = tf.keras.utils.to_categorical(test_label)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28)))  # 28*28
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
model.summary()
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
              loss='categorical_crossentropy',
              metrics=['acc']
)
model.fit(train_image, train_label_onehot, epochs=5)

Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_3 (Flatten)          (None, 784)               0         
_________________________________________________________________
dense_6 (Dense)              (None, 128)               100480    
_________________________________________________________________
dense_7 (Dense)              (None, 10)                1290      
=================================================================
Total params: 101,770
Trainable params: 101,770
Non-trainable params: 0
_________________________________________________________________
Train on 60000 samples
Epoch 1/5
60000/60000 [==============================] - 9s 148us/sample - loss: 3.9736 - acc: 0.7085
Epoch 2/5
60000/60000 [==============================] - 7s 122us/sample - loss: 0.6800 - acc: 0.7587
Epoch 3/5
60000/60000 [==============================] - 8s 135us/sample - loss: 0.6008 - acc: 0.7850
Epoch 4/5
60000/60000 [==============================] - 7s 119us/sample - loss: 0.5508 - acc: 0.8023
Epoch 5/5
60000/60000 [==============================] - 8s 125us/sample - loss: 0.5175 - acc: 0.8154
<tensorflow.python.keras.callbacks.History at 0x2fe4bf91388>

#评价model.evaluate(test_image, test_label_onehot)

1s 72us/sample - loss: 0.3707 - acc: 0.8031

[0.5642493257284165, 0.8031]

3.6 多添加几层隐藏层

model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28)))  # 28*28
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
model.summary()

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_2 (Flatten)          (None, 784)               0         
_________________________________________________________________
dense_4 (Dense)              (None, 128)               100480    
_________________________________________________________________
dense_5 (Dense)              (None, 128)               16512     
_________________________________________________________________
dense_6 (Dense)              (None, 128)               16512     
_________________________________________________________________
dense_7 (Dense)              (None, 10)                1290      
=================================================================
Total params: 134,794
Trainable params: 134,794
Non-trainable params: 0
_________________________________________________________________

model.compile(optimizer="adam",
        loss='categorical_crossentropy',
        metrics=["acc"])
        #加入验证集
history = model.fit(train_image,train_label_onehot,epochs=20,validation_data=(test_image,test_label_onehot))
plt.plot(history.epoch,history.history.get("acc"),label="train_acc")
plt.plot(history.epoch,history.history.get("val_acc"),label="val_acc")
plt.legend()
plt.show()

从图中，我们可训练集的准确率，在不断上升，而验证集的数据没有上升，甚至还有些下降，这是典型的过拟合状态，常见的，我们可以采用丢弃法或者减少层数来抑制过拟合状态。

3.7 添加dropout

model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28)))  # 28*28
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
model.compile(optimizer="adam",
        loss='categorical_crossentropy',
        metrics=["acc"])
#加入验证集
history = model.fit(train_image,train_label_onehot,epochs=20,validation_data=(test_image,test_label_onehot))
plt.plot(history.epoch,history.history.get("acc"),label="train_acc")
plt.plot(history.epoch,history.history.get("val_acc"),label="val_acc")
plt.legend()
plt.show()