TensorFlow2教程14：基础MLP网络
TensorFlow2教程-基础MLP网络
1.回归任务
# 导入数据
(x_train, y_train), (x_test, y_test) = keras.datasets.boston_housing.load_data()
print(x_train.shape, ' ', y_train.shape)
print(x_test.shape, ' ', y_test.shape)
(404, 13) (404,)
(102, 13) (102,)
# 构建模型
model = keras.Sequential([
layers.Dense(32, activation='sigmoid', input_shape=(13,)),
layers.Dense(32, activation='sigmoid'),
layers.Dense(32, activation='sigmoid'),
layers.Dense(1)
])
# 配置模型
model.compile(optimizer=keras.optimizers.SGD(0.1),
loss='mean_squared_error', # keras.losses.mean_squared_error
metrics=['mse'])
model.summary()
Model: "sequential_10"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_33 (Dense) (None, 32) 448
_________________________________________________________________
dense_34 (Dense) (None, 32) 1056
_________________________________________________________________
dense_35 (Dense) (None, 32) 1056
_________________________________________________________________
dense_36 (Dense) (None, 1) 33
=================================================================
Total params: 2,593
Trainable params: 2,593
Non-trainable params: 0
_________________________________________________________________
# 训练
model.fit(x_train, y_train, batch_size=50, epochs=50, validation_split=0.1, verbose=1)
Train on 363 samples, validate on 41 samples
Epoch 1/50
363/363 [==============================] - 0s 430us/sample - loss: 371.0176 - mse: 371.0175 - val_loss: 50.0381 - val_mse: 50.0381
Epoch 50/50
363/363 [==============================] - 0s 28us/sample - loss: 80.1490 - mse: 80.1490 - val_loss: 30.6706 - val_mse: 30.6706
result = model.evaluate(x_test, y_test)
102/102 [==============================] - 0s 116us/sample - loss: 75.0492 - mse: 75.0492
print(model.metrics_names)
print(result)
['loss', 'mse']
[75.04923741957721, 75.04924]
2.分类任务
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
whole_data = load_breast_cancer()
x_data = whole_data.data
y_data = whole_data.target
x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.3, random_state=7)
print(x_train.shape, ' ', y_train.shape)
print(x_test.shape, ' ', y_test.shape)
(398, 30) (398,)
(171, 30) (171,)
# 构建模型无锡人流医院哪家好 http://www.wxbhnkyy120.com/
model = keras.Sequential([
layers.Dense(32, activation='relu', input_shape=(30,)),
layers.Dense(32, activation='relu'),
layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer=keras.optimizers.Adam(),
loss=keras.losses.binary_crossentropy,
metrics=['accuracy'])
model.summary()
Model: "sequential_14"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_46 (Dense) (None, 32) 992
_________________________________________________________________
dense_47 (Dense) (None, 32) 1056
_________________________________________________________________
dense_48 (Dense) (None, 1) 33
=================================================================
Total params: 2,081
Trainable params: 2,081
Non-trainable params: 0
_________________________________________________________________
model.fit(x_train, y_train, batch_size=64, epochs=10, verbose=1)
Epoch 10/10
398/398 [==============================] - 0s 27us/sample - loss: 0.2597 - accuracy: 0.9045
model.evaluate(x_test, y_test)
171/171 [==============================] - 0s 463us/sample - loss: 0.3877 - accuracy: 0.8772
[0.38765583248340596, 0.877193]
print(model.metrics_names)
['loss', 'accuracy']
