深度学习笔记-----基于TensorFlow2.2.0代码练习（第五课）
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写在正文之前：
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这篇紧接着上一篇的博文
深度学习笔记-----基于TensorFlow2.2.0代码练习（第三课&第四课）
主要写的是TensorFlow2.0的代码练习，跟随着[KGP Talkie的【TensorFlow 2.0】实战进阶教程]进行学习，并将其中一些不适用的代码错误进行修改。
本文跟随视频油管非常火的【TensorFlow 2.0】实战进阶教程（中英字幕+代码实战）第五课
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首先，需要有登登登的能力，因为这是在goodle colab上进行编写。除此之外，需要有一个谷歌的账号。
为以后需要，登陆谷歌云
点击新建按钮，新建一个colab文件夹，这样会和你的colab进行同步保存。
第一次时候，需要在更多----关联更多应用-----加载colabpratory
正文
!pip install tensorflow-gpu
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import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Flatten, Dense, Conv2D, MaxPool2D, Dropout
原始错误：
File “”, line 2
from tensorflow.keras.models import Sequential()
^
SyntaxError: invalid syntax
此问题是因为运行from tensorflow.keras import Sequential()出错解决办法将()删除
print(tf.__version__)
2.2.0
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from tensorflow.keras.datasets import cifar10
(X_train, y_train),(X_test, y_test)= cifar10.load_data()
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
170500096/170498071 [==============================] - 3s 0us/step
class_name = ['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck']
#归一化
X_train = X_train/255
X_test = X_test/255
#读取训练和测试集的大小
X_train.shape
X_test.shape
(10000, 32, 32, 3)
建立CNN模型
model = Sequential()#获取模型
#添加第一个卷积层
model.add(Conv2D(filters=32, kernel_size=(3,3),padding='same',activation='relu',input_shape = [32,32,3]))
#添加第二个卷积层
model.add(Conv2D(filters=32, kernel_size=(3,3),padding='same',activation='relu'))
#添加池化层
model.add(MaxPool2D(pool_size=(2,2),strides=2, padding='valid'))
#使用dropout防止过拟合
model.add(Dropout(0.5))
#添加CF
model.add(Flatten())
model.add(Dense(units=128, activation='relu'))
#添加输出层
model.add(Dense(units=10, activation='softmax'))
#打印模型的概要
model.summary()
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_3 (Conv2D) (None, 32, 32, 32) 896
_________________________________________________________________
conv2d_4 (Conv2D) (None, 32, 32, 32) 9248
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 16, 16, 32) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 8192) 0
_________________________________________________________________
dense_2 (Dense) (None, 128) 1048704
_________________________________________________________________
dense_3 (Dense) (None, 10) 1290
=================================================================
Total params: 1,060,138
Trainable params: 1,060,138
Non-trainable params: 0
_________________________________________________________________
建立优化模型
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['sparse_categorical_accuracy'])
拟合数据
history = model.fit(X_train,y_train,batch_size=10,epochs=10,verbose=1,validation_data=(X_test,y_test))
Epoch 1/10
5000/5000 [==============================] - 193s 39ms/step - loss: 1.3741 - sparse_categorical_accuracy: 0.5066 - val_loss: 1.1207 - val_sparse_categorical_accuracy: 0.5974
Epoch 2/10
5000/5000 [==============================] - 194s 39ms/step - loss: 1.0584 - sparse_categorical_accuracy: 0.6255 - val_loss: 0.9787 - val_sparse_categorical_accuracy: 0.6551
Epoch 3/10
5000/5000 [==============================] - 198s 40ms/step - loss: 0.9279 - sparse_categorical_accuracy: 0.6737 - val_loss: 0.9196 - val_sparse_categorical_accuracy: 0.6789
Epoch 4/10
5000/5000 [==============================] - 192s 38ms/step - loss: 0.8342 - sparse_categorical_accuracy: 0.7046 - val_loss: 0.9135 - val_sparse_categorical_accuracy: 0.6815
Epoch 5/10
5000/5000 [==============================] - 192s 38ms/step - loss: 0.7624 - sparse_categorical_accuracy: 0.7309 - val_loss: 0.8846 - val_sparse_categorical_accuracy: 0.6934
Epoch 6/10
5000/5000 [==============================] - 194s 39ms/step - loss: 0.6978 - sparse_categorical_accuracy: 0.7528 - val_loss: 0.9018 - val_sparse_categorical_accuracy: 0.6914
Epoch 7/10
5000/5000 [==============================] - 194s 39ms/step - loss: 0.6547 - sparse_categorical_accuracy: 0.7680 - val_loss: 0.8921 - val_sparse_categorical_accuracy: 0.6999
Epoch 8/10
5000/5000 [==============================] - 193s 39ms/step - loss: 0.6064 - sparse_categorical_accuracy: 0.7841 - val_loss: 0.8963 - val_sparse_categorical_accuracy: 0.6906
Epoch 9/10
5000/5000 [==============================] - 194s 39ms/step - loss: 0.5679 - sparse_categorical_accuracy: 0.7990 - val_loss: 0.8977 - val_sparse_categorical_accuracy: 0.6981
Epoch 10/10
5000/5000 [==============================] - 193s 39ms/step - loss: 0.5351 - sparse_categorical_accuracy: 0.8106 - val_loss: 0.9061 - val_sparse_categorical_accuracy: 0.6992
绘制曲线
epoch_range = range(1,11)
plt.plot(epoch_range, history.history['sparse_categorical_accuracy'])
plt.plot(epoch_range, history.history['val_sparse_categorical_accuracy'])
plt.title('Model Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(['Train','Val'], loc ='upper left')
plt.show()
plt.plot(epoch_range, history.history['loss'])
plt.plot(epoch_range, history.history['val_loss'])
plt.title('Model Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend(['Train','Val'], loc ='upper left')
plt.show()
验证集在3次训练之后，loss并没有太大下降，表明，其没有学习
绘制confusion 矩阵
from mlxtend.plotting import plot_confusion_matrix
from sklearn.metrics import confusion_matrix
y_pred = model.predict_classes(X_test)
WARNING:tensorflow:From :1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.
Instructions for updating:
Please use instead:* `np.argmax(model.predict(x), axis=-1)`, if your model does multi-class classification (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`, if your model does binary classification (e.g. if it uses a `sigmoid` last-layer activation).
y_pred
array([5, 8, 8, ..., 5, 1, 7])
mat = confusion_matrix(y_test,y_pred)
plot_confusion_matrix(conf_mat= mat,figsize=(8,8),class_names=class_name,show_normed=True)
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