TensorFlow2教程15：mlp及深度学习常见技巧

技术问题解决：提前学习常见软件教程 #生活技巧# #工作学习技巧# #远程工作技巧#

　　我们将以mlp对为，基础模型，然后介绍一些深度学习常见技巧， 如：

　　权重初始化， 激活函数， 优化器， 批规范化， dropout，模型集成

　　1.导入数据

　　(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

　　x_train = x_train.reshape([x_train.shape[0], -1])

　　x_test = x_test.reshape([x_test.shape[0], -1])

　　print(x_train.shape, ' ', y_train.shape)

　　print(x_test.shape, ' ', y_test.shape)

　　(60000, 784) (60000,)

　　(10000, 784) (10000,)

　　2.基础模型

　　model = keras.Sequential([

　　layers.Dense(64, activation='relu', input_shape=(784,)),

　　layers.Dense(64, activation='relu'),

　　layers.Dense(64, activation='relu'),

　　layers.Dense(10, activation='softmax')

　　])

　　model.compile(optimizer=keras.optimizers.Adam(),

　　loss=keras.losses.SparseCategoricalCrossentropy(),

　　metrics=['accuracy'])

　　model.summary()

　　Model: "sequential_3"

　　_________________________________________________________________

　　Layer (type) Output Shape Param #

　　=================================================================

　　dense_11 (Dense) (None, 64) 50240

　　_________________________________________________________________

　　dense_12 (Dense) (None, 64) 4160

　　_________________________________________________________________

　　dense_13 (Dense) (None, 64) 4160

　　_________________________________________________________________

　　dense_14 (Dense) (None, 10) 650

　　=================================================================

　　Total params: 59,210

　　Trainable params: 59,210

　　Non-trainable params: 0

　　_________________________________________________________________

　　history = model.fit(x_train, y_train, batch_size=256, epochs=100, validation_split=0.3, verbose=0)

　　import matplotlib.pyplot as plt

　　plt.plot(history.history['accuracy'])

　　plt.plot(history.history['val_accuracy'])

　　plt.legend(['training', 'validation'], loc='upper left')

　　plt.show()

　　result = model.evaluate(x_test, y_test)

　　10000/10000 [==============================] - 0s 25us/sample - loss: 0.4429 - accuracy: 0.9632

　　3.权重初始化

　　model = keras.Sequential([

　　layers.Dense(64, activation='relu', kernel_initializer='he_normal', input_shape=(784,)),

　　layers.Dense(64, activation='relu', kernel_initializer='he_normal'),

　　layers.Dense(64, activation='relu', kernel_initializer='he_normal'),

　　layers.Dense(10, activation='softmax')

　　])

　　model.compile(optimizer=keras.optimizers.Adam(),

　　loss=keras.losses.SparseCategoricalCrossentropy(),

　　metrics=['accuracy'])

　　model.summary()

　　Model: "sequential_5"

　　_________________________________________________________________

　　Layer (type) Output Shape Param #

　　=================================================================

　　dense_19 (Dense) (None, 64) 50240

　　_________________________________________________________________

　　dense_20 (Dense) (None, 64) 4160

　　_________________________________________________________________

　　dense_21 (Dense) (None, 64) 4160

　　_________________________________________________________________

　　dense_22 (Dense) (None, 10) 650

　　=================================================================

　　Total params: 59,210

　　Trainable params: 59,210

　　Non-trainable params: 0

　　_________________________________________________________________

　　history = model.fit(x_train, y_train, batch_size=256, epochs=100, validation_split=0.3, verbose=0)

　　import matplotlib.pyplot as plt

　　plt.plot(history.history['accuracy'])

　　plt.plot(history.history['val_accuracy'])

　　plt.legend(['training', 'validation'], loc='upper left')

　　plt.show()

　　result = model.evaluate(x_test, y_test)

　　10000/10000 [==============================] - 0s 21us/sample - loss: 0.4355 - accuracy: 0.9587

　　4.激活函数

　　relu和sigmoid对比

　　model = keras.Sequential([

　　layers.Dense(64, activation='sigmoid', input_shape=(784,)),

　　layers.Dense(64, activation='sigmoid'),

　　layers.Dense(64, activation='sigmoid'),

　　layers.Dense(10, activation='softmax')

　　])

　　model.compile(optimizer=keras.optimizers.Adam(),

　　loss=keras.losses.SparseCategoricalCrossentropy(),

　　metrics=['accuracy'])

　　model.summary()

　　Model: "sequential_6"

　　_________________________________________________________________

　　Layer (type) Output Shape Param #

　　=================================================================

　　dense_23 (Dense) (None, 64) 50240

　　_________________________________________________________________

　　dense_24 (Dense) (None, 64) 4160

　　_________________________________________________________________

　　dense_25 (Dense) (None, 64) 4160

　　_________________________________________________________________

　　dense_26 (Dense) (None, 10) 650

　　=================================================================

　　Total params: 59,210

　　Trainable params: 59,210

　　Non-trainable params: 0

　　_________________________________________________________________

　　history = model.fit(x_train, y_train, batch_size=256, epochs=100, validation_split=0.3, verbose=0)

　　plt.plot(history.history['accuracy'])

　　plt.plot(history.history['val_accuracy'])

　　plt.legend(['training', 'validation'], loc='upper left')

　　plt.show()

　　result = model.evaluate(x_test, y_test)

　　10000/10000 [==============================] - 0s 29us/sample - loss: 0.1526 - accuracy: 0.9529

　　5.优化器

　　model = keras.Sequential([

　　layers.Dense(64, activation='sigmoid', input_shape=(784,)),

　　layers.Dense(64, activation='sigmoid'),

　　layers.Dense(64, activation='sigmoid'),

　　layers.Dense(10, activation='softmax')

　　])

　　model.compile(optimizer=keras.optimizers.SGD(),

　　loss=keras.losses.SparseCategoricalCrossentropy(),

　　metrics=['accuracy'])

　　model.summary()

　　Model: "sequential_7"

　　_________________________________________________________________

　　Layer (type) Output Shape Param #

　　=================================================================

　　dense_27 (Dense) (None, 64) 50240

　　_________________________________________________________________

　　dense_28 (Dense) (None, 64) 4160

　　_________________________________________________________________

　　dense_29 (Dense) (None, 64) 4160

　　_________________________________________________________________

　　dense_30 (Dense) (None, 10) 650

　　=================================================================

　　Total params: 59,210

　　Trainable params: 59,210

　　Non-trainable params: 0

　　_________________________________________________________________

　　history = model.fit(x_train, y_train, batch_size=256, epochs=100, validation_split=0.3, verbose=0)

　　plt.plot(history.history['accuracy'])

　　plt.plot(history.history['val_accuracy'])

　　plt.legend(['training', 'validation'], loc='upper left')

　　plt.show()

　　result = model.evaluate(x_test, y_test)

　　10000/10000 [==============================] - 0s 44us/sample - loss: 2.1199 - accuracy: 0.4749

　　6.批正则化

　　model = keras.Sequential([

　　layers.Dense(64, activation='relu', input_shape=(784,)),

　　layers.BatchNormalization(),

　　layers.Dense(64, activation='relu'),

　　layers.BatchNormalization(),

　　layers.Dense(64, activation='relu'),

　　layers.BatchNormalization(),

　　layers.Dense(10, activation='softmax')

　　])

　　model.compile(optimizer=keras.optimizers.SGD(),

　　loss=keras.losses.SparseCategoricalCrossentropy(),

　　metrics=['accuracy'])

　　model.summary()

　　Model: "sequential_8"

　　_________________________________________________________________

　　Layer (type) Output Shape Param #

　　=================================================================

　　dense_31 (Dense) (None, 64) 50240

　　_________________________________________________________________

　　batch_normalization_v2 (Batc (None, 64) 256

　　_________________________________________________________________

　　dense_32 (Dense) (None, 64) 4160

　　_________________________________________________________________

　　batch_normalization_v2_1 (Ba (None, 64) 256

　　_________________________________________________________________

　　dense_33 (Dense) (None, 64) 4160

　　_________________________________________________________________

　　batch_normalization_v2_2 (Ba (None, 64) 256

　　_________________________________________________________________

　　dense_34 (Dense) (None, 10) 650

　　=================================================================

　　Total params: 59,978

　　Trainable params: 59,594

　　Non-trainable params: 384

　　_________________________________________________________________

　　history = model.fit(x_train, y_train, batch_size=256, epochs=100, validation_split=0.3, verbose=0)

　　plt.plot(history.history['accuracy'])

　　plt.plot(history.history['val_accuracy'])

　　plt.legend(['training', 'validation'], loc='upper left')

　　plt.show()

　　result = model.evaluate(x_test, y_test)

　　10000/10000 [==============================] - 0s 25us/sample - loss: 0.1863 - accuracy: 0.9447

　　7.dropout 

　　model = keras.Sequential([

　　layers.Dense(64, activation='relu', input_shape=(784,)),

　　layers.Dropout(0.2),

　　layers.Dense(64, activation='relu'),

　　layers.Dropout(0.2),

　　layers.Dense(64, activation='relu'),

　　layers.Dropout(0.2),

　　layers.Dense(10, activation='softmax')

　　])无锡妇科医院哪家好 http://www.ytsgfk120.com/

　　model.compile(optimizer=keras.optimizers.SGD(),

　　loss=keras.losses.SparseCategoricalCrossentropy(),

　　metrics=['accuracy'])

　　model.summary()

　　Model: "sequential_9"

　　_________________________________________________________________

　　Layer (type) Output Shape Param #

　　=================================================================

　　dense_35 (Dense) (None, 64) 50240

　　_________________________________________________________________

　　dropout (Dropout) (None, 64) 0

　　_________________________________________________________________

　　dense_36 (Dense) (None, 64) 4160

　　_________________________________________________________________

　　dropout_1 (Dropout) (None, 64) 0

　　_________________________________________________________________

　　dense_37 (Dense) (None, 64) 4160

　　_________________________________________________________________

　　dropout_2 (Dropout) (None, 64) 0

　　_________________________________________________________________

　　dense_38 (Dense) (None, 10) 650

　　=================================================================

　　Total params: 59,210

　　Trainable params: 59,210

　　Non-trainable params: 0

　　_________________________________________________________________

　　history = model.fit(x_train, y_train, batch_size=256, epochs=100, validation_split=0.3, verbose=0)

　　plt.plot(history.history['accuracy'])

　　plt.plot(history.history['val_accuracy'])

　　plt.legend(['training', 'validation'], loc='upper left')

　　plt.show()

　　result = model.evaluate(x_test, y_test)

　　10000/10000 [==============================] - 0s 27us/sample - loss: 0.6157 - accuracy: 0.8132

　　8.模型集成

　　下面是使用投票的方法进行模型集成

　　import numpy as np

　　from tensorflow.keras.wrappers.scikit_learn import KerasClassifier

　　from sklearn.ensemble import VotingClassifier

　　from sklearn.metrics import accuracy_score

　　def mlp_model():

　　model = keras.Sequential([

　　layers.Dense(64, activation='relu', input_shape=(784,)),

　　layers.Dropout(0.2),

　　layers.Dense(64, activation='relu'),

　　layers.Dropout(0.2),

　　layers.Dense(64, activation='relu'),

　　layers.Dropout(0.2),

　　layers.Dense(10, activation='softmax')

　　])

　　model.compile(optimizer=keras.optimizers.SGD(),

　　loss=keras.losses.SparseCategoricalCrossentropy(),

　　metrics=['accuracy'])

　　return model

　　model1 = KerasClassifier(build_fn=mlp_model, epochs=100, verbose=0)

　　model2 = KerasClassifier(build_fn=mlp_model, epochs=100, verbose=0)

　　model3 = KerasClassifier(build_fn=mlp_model, epochs=100, verbose=0)

　　ensemble_clf = VotingClassifier(estimators=[

　　('model1', model1), ('model2', model2), ('model3', model3)

　　], voting='soft')

　　ensemble_clf.fit(x_train, y_train)

　　VotingClassifier(estimators=[('model1', ), ('model2', ), ('model3', )],

　　flatten_transform=None, n_jobs=None, voting='soft', weights=None)

　　y_pred = ensemble_clf.predict(x_test)

　　print('acc: ', accuracy_score(y_pred, y_test))

　　acc: 0.9504

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