深度學習筆記-----基於TensorFlow2.2.0代碼練習（第五課）

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寫在正文之前：

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這篇緊接着上一篇的博文
深度學習筆記-----基於TensorFlow2.2.0代碼練習（第三課&第四課）
主要寫的是TensorFlow2.0的代碼練習，跟隨着[KGP Talkie的【TensorFlow 2.0】實戰進階教程]進行學習，並將其中一些不適用的代碼錯誤進行修改。
本文跟隨視頻油管非常火的【TensorFlow 2.0】實戰進階教程（中英字幕+代碼實戰）第五課
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首先，需要有登登登的能力，因爲這是在goodle colab上進行編寫。除此之外，需要有一個谷歌的賬號。
爲以後需要，登陸谷歌雲

點擊新建按鈕，新建一個colab文件夾，這樣會和你的colab進行同步保存。
第一次時候，需要在更多----關聯更多應用-----加載colabpratory

正文

!pip install tensorflow-gpu

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!pip install mlxtend==0.17.0

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import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Flatten, Dense, Conv2D, MaxPool2D, Dropout

原始錯誤：
File “”, line 2
from tensorflow.keras.models import Sequential()
^
SyntaxError: invalid syntax
此問題是因爲運行from tensorflow.keras import Sequential()出錯解決辦法將()刪除

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']

#歸一化
X_train = X_train/255
X_test = X_test/255

#讀取訓練和測試集的大小
X_train.shape
X_test.shape

(10000, 32, 32, 3)

建立CNN模型

model = Sequential()#獲取模型
#添加第一個卷積層
model.add(Conv2D(filters=32, kernel_size=(3,3),padding='same',activation='relu',input_shape = [32,32,3]))
#添加第二個卷積層
model.add(Conv2D(filters=32, kernel_size=(3,3),padding='same',activation='relu'))
#添加池化層
model.add(MaxPool2D(pool_size=(2,2),strides=2, padding='valid'))
#使用dropout防止過擬合
model.add(Dropout(0.5))
#添加CF
model.add(Flatten())
model.add(Dense(units=128, activation='relu'))
#添加輸出層
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
_________________________________________________________________

建立優化模型

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

擬合數據

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

繪製曲線

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()

驗證集在3次訓練之後，loss並沒有太大下降，表明，其沒有學習

繪製confusion 矩陣

from mlxtend.plotting import plot_confusion_matrix
from sklearn.metrics import confusion_matrix

y_pred = model.predict_classes(X_test)

WARNING:tensorflow:From <ipython-input-31-fdaaf2360398>: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)

(<Figure size 576x576 with 1 Axes>,
 <matplotlib.axes._subplots.AxesSubplot at 0x7f439c89fc18>)