import numpy as np

from keras.datasets import mnist

from keras.models import Sequential

from keras.layers import Dense, Dropout, Activation, Flatten

from keras.layers import Convolution2D, MaxPooling2D

from keras.utils import np_utils

# sklean接口的包装器KerasClassifier，作为sklearn的分类器接口

from keras.wrappers.scikit_learn import KerasClassifier

# 穷搜所有特定的参数值选出最好的模型参数

from sklearn.grid_search import GridSearchCV


Using TensorFlow backend.


# 类别的数目

nb_classes = 10

# 输入图像的维度

img_rows, img_cols = 28, 28

# 读取数据

(X_train, y_train), (X_test, y_test) = mnist.load_data()

# 读取的数据不包含通道维，因此shape为(60000,28,28)

# 为了保持和后端tensorflow的数据格式一致，将数据补上通道维

X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)

X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)

# 新的数据shape为 (60000,28,28,1)， 1代表通道是1，也就是灰阶图片

# 指明输入数据的大小，便于后面搭建网络的第一层传入该参数

input_shape = (img_rows, img_cols, 1)

# 数据类型改为float32，单精度浮点数

X_train = X_train.astype('float32')

X_test = X_test.astype('float32')

# 数据归一化（图像数据常用）

X_train /= 255

X_test /= 255

# 将类别标签转换为one-hot编码

y_train = np_utils.to_categorical(y_train, nb_classes)

y_test = np_utils.to_categorical(y_test, nb_classes)


# 定义配置卷积网络模型的函数

def make_model(dense_layer_sizes, nb_filters, nb_conv, nb_pool):

    model = Sequential()

    model.add(Convolution2D(nb_filters, nb_conv, nb_conv,

                            border_mode='valid',

                            input_shape=input_shape))

    model.add(Activation('relu'))

    model.add(Convolution2D(nb_filters, nb_conv, nb_conv))

    model.add(Activation('relu'))

    model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))

    model.add(Dropout(0.25))

    model.add(Flatten())

    for layer_size in dense_layer_sizes:

        model.add(Dense(layer_size))

    model.add(Activation('relu'))

    model.add(Dropout(0.5))

    model.add(Dense(nb_classes))

    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy',

                  optimizer='adadelta',

                  metrics=['accuracy'])

    return model


# 全连接层的备选参数列表

dense_size_candidates = [[32], [64], [32, 32], [64, 64]]

# 实现为Keras准备的sklearn分类器接口，创建一个分类器/评估器对象

# 传入的参数为：

# build_fn: callable function or class instance

# **sk_params: model parameters & fitting parameters

# 具体分析如下：

# 传入的第一个参数(build_fn)为可回调的函数，该函数建立、配置并返回一个Keras model，

# 该model将被用来训练/预测，这里我们传入了刚刚定义好的make_model函数

# 传入的第二个参数(**sk_params)为关键字参数(关键字参数在函数内部自动组装为一个dict),

# 既可以是模型的参数，也可以是训练的参数，合法的模型参数就是build_fn的参数，

# 注意，像所有sklearn中其他的评估器(estimator)一样，build_fn应当为其参数提供默认值，

# 以便我们在建立estimator的时候不用向sk_params传入任何值。

# sk_params也可以接收用来调用fit/predict/predict_proba/score方法的参数，

# 例如'nb_epoch','batch_size'

# fit/predict/predict_proba/score方法的参数将会优先从传入fit/predict/predict_proba/score

# 的字典参数中选择，其次才从传入sk_params的参数中选，最后才选择keras的Sequential模型的默认参数中选择

# 这里我们传入了用于调用fit方法的batch_size参数

my_classifier = KerasClassifier(make_model, batch_size=32)

# 当调用sklearn的grid_search接口时，合法的可调参数就是传给sk_params的参数，包括训练参数

# 换句话说，就是可以用grid_search来选择最佳的batch_size/nb_epoch，或者其他的一些模型参数

# GridSearchCV类，穷搜(Exhaustive search)评估器中所有特定的参数，

# 其重要的两类方法为fit和predict

# 传入参数为评估器对象my_classifier，由每一个grid point实例化一个estimator

# 参数网格param_grid，类型为dict，需要尝试的参数名称以及对应的数值

# 评估方式scoring，这里采用对数损失来评估

validator = GridSearchCV(my_classifier,

                         param_grid={'dense_layer_sizes': dense_size_candidates,

                                     'nb_epoch': [3,6],

                                     'nb_filters': [8],

                                     'nb_conv': [3],

                                     'nb_pool': [2]},

                         scoring='log_loss')

# 根据各个参数值的不同组合在(X_train, y_train)上训练模型

validator.fit(X_train, y_train)

# 打印出训练过程中找到的最佳参数

print('Yhe parameters of the best model are: ')

print(validator.best_params_)


Epoch 1/3

40000/40000 [==============================] - 14s - loss: 0.8058 - acc: 0.7335

Epoch 2/3

40000/40000 [==============================] - 10s - loss: 0.4620 - acc: 0.8545

Epoch 3/3

40000/40000 [==============================] - 10s - loss: 0.3958 - acc: 0.8747

19776/20000 [============================>.] - ETA: 0sEpoch 1/3

40000/40000 [==============================] - 11s - loss: 0.9589 - acc: 0.6804

Epoch 2/3

40000/40000 [==============================] - 10s - loss: 0.5885 - acc: 0.8116

Epoch 3/3

40000/40000 [==============================] - 10s - loss: 0.5021 - acc: 0.8429

19488/20000 [============================>.] - ETA: 0sEpoch 1/3

40000/40000 [==============================] - 11s - loss: 0.9141 - acc: 0.6958

Epoch 2/3

40000/40000 [==============================] - 10s - loss: 0.5716 - acc: 0.8136

Epoch 3/3

40000/40000 [==============================] - 10s - loss: 0.4515 - acc: 0.8547

19584/20000 [============================>.] - ETA: 0sEpoch 1/6

40000/40000 [==============================] - 11s - loss: 0.8968 - acc: 0.6983

Epoch 2/6

40000/40000 [==============================] - 10s - loss: 0.5692 - acc: 0.8130

Epoch 3/6

40000/40000 [==============================] - 10s - loss: 0.4600 - acc: 0.8494

Epoch 4/6

40000/40000 [==============================] - 10s - loss: 0.4091 - acc: 0.8694

Epoch 5/6

40000/40000 [==============================] - 10s - loss: 0.3717 - acc: 0.8790

Epoch 6/6

40000/40000 [==============================] - 10s - loss: 0.3461 - acc: 0.8898

20000/20000 [==============================] - 1s

Epoch 1/6

40000/40000 [==============================] - 11s - loss: 0.8089 - acc: 0.7310

Epoch 2/6

40000/40000 [==============================] - 10s - loss: 0.4770 - acc: 0.8498

Epoch 3/6

40000/40000 [==============================] - 10s - loss: 0.4086 - acc: 0.8704

Epoch 4/6

40000/40000 [==============================] - 10s - loss: 0.3657 - acc: 0.8860

Epoch 5/6

40000/40000 [==============================] - 10s - loss: 0.3383 - acc: 0.8938

Epoch 6/6

40000/40000 [==============================] - 10s - loss: 0.3164 - acc: 0.9027

19520/20000 [============================>.] - ETA: 0sEpoch 1/6

40000/40000 [==============================] - 11s - loss: 0.8393 - acc: 0.7214

Epoch 2/6

40000/40000 [==============================] - 10s - loss: 0.5132 - acc: 0.8379

Epoch 3/6

40000/40000 [==============================] - 10s - loss: 0.4331 - acc: 0.8635

Epoch 4/6

40000/40000 [==============================] - 10s - loss: 0.3813 - acc: 0.8808

Epoch 5/6

40000/40000 [==============================] - 10s - loss: 0.3530 - acc: 0.8902

Epoch 6/6

40000/40000 [==============================] - 10s - loss: 0.3278 - acc: 0.8986

19936/20000 [============================>.] - ETA: 0sEpoch 1/3

40000/40000 [==============================] - 11s - loss: 0.5975 - acc: 0.8099

Epoch 2/3

40000/40000 [==============================] - 10s - loss: 0.3181 - acc: 0.9048

Epoch 3/3

40000/40000 [==============================] - 10s - loss: 0.2673 - acc: 0.9199

19808/20000 [============================>.] - ETA: 0sEpoch 1/3

40000/40000 [==============================] - 11s - loss: 0.6155 - acc: 0.8040

Epoch 2/3

40000/40000 [==============================] - 11s - loss: 0.3500 - acc: 0.8951

Epoch 3/3

40000/40000 [==============================] - 11s - loss: 0.2864 - acc: 0.9156

20000/20000 [==============================] - 1s

Epoch 1/3

40000/40000 [==============================] - 11s - loss: 0.7519 - acc: 0.7560

Epoch 2/3

40000/40000 [==============================] - 10s - loss: 0.4660 - acc: 0.8580

Epoch 3/3

40000/40000 [==============================] - 10s - loss: 0.3553 - acc: 0.8936

19776/20000 [============================>.] - ETA: 0sEpoch 1/6

40000/40000 [==============================] - 11s - loss: 0.5869 - acc: 0.8162

Epoch 2/6

40000/40000 [==============================] - 11s - loss: 0.3279 - acc: 0.9014

Epoch 3/6

40000/40000 [==============================] - 11s - loss: 0.2725 - acc: 0.9187

Epoch 4/6

40000/40000 [==============================] - 11s - loss: 0.2366 - acc: 0.9291

Epoch 5/6

40000/40000 [==============================] - 11s - loss: 0.2102 - acc: 0.9386

Epoch 6/6

40000/40000 [==============================] - 16s - loss: 0.1954 - acc: 0.9423

19840/20000 [============================>.] - ETA: 0sEpoch 1/6

40000/40000 [==============================] - 11s - loss: 0.5526 - acc: 0.8262

Epoch 2/6

40000/40000 [==============================] - 11s - loss: 0.2903 - acc: 0.9142

Epoch 3/6

40000/40000 [==============================] - 11s - loss: 0.2361 - acc: 0.9302

Epoch 4/6

40000/40000 [==============================] - 11s - loss: 0.2064 - acc: 0.9396

Epoch 5/6

40000/40000 [==============================] - 10s - loss: 0.1886 - acc: 0.9443

Epoch 6/6

40000/40000 [==============================] - 10s - loss: 0.1755 - acc: 0.9496

19808/20000 [============================>.] - ETA: 0sEpoch 1/6

40000/40000 [==============================] - 11s - loss: 0.7275 - acc: 0.7677

Epoch 2/6

40000/40000 [==============================] - 10s - loss: 0.4141 - acc: 0.8772

Epoch 3/6

40000/40000 [==============================] - 10s - loss: 0.3136 - acc: 0.9056

Epoch 4/6

40000/40000 [==============================] - 10s - loss: 0.2651 - acc: 0.9210

Epoch 5/6

40000/40000 [==============================] - 10s - loss: 0.2363 - acc: 0.9306

Epoch 6/6

40000/40000 [==============================] - 10s - loss: 0.2092 - acc: 0.9380

19552/20000 [============================>.] - ETA: 0sEpoch 1/3

40000/40000 [==============================] - 12s - loss: 0.7849 - acc: 0.7334

Epoch 2/3

40000/40000 [==============================] - 11s - loss: 0.4506 - acc: 0.8587

Epoch 3/3

40000/40000 [==============================] - 11s - loss: 0.3741 - acc: 0.8813

19872/20000 [============================>.] - ETA: 0sEpoch 1/3

40000/40000 [==============================] - 12s - loss: 0.8744 - acc: 0.7068

Epoch 2/3

40000/40000 [==============================] - 11s - loss: 0.5231 - acc: 0.8312

Epoch 3/3

40000/40000 [==============================] - 11s - loss: 0.4305 - acc: 0.8635

19552/20000 [============================>.] - ETA: 0sEpoch 1/3

40000/40000 [==============================] - 12s - loss: 0.7567 - acc: 0.7473

Epoch 2/3

40000/40000 [==============================] - 11s - loss: 0.4200 - acc: 0.8685

Epoch 3/3

40000/40000 [==============================] - 11s - loss: 0.3604 - acc: 0.8887

19712/20000 [============================>.] - ETA: 0sEpoch 1/6

40000/40000 [==============================] - 12s - loss: 0.7111 - acc: 0.7676

Epoch 2/6

40000/40000 [==============================] - 11s - loss: 0.4243 - acc: 0.8669

Epoch 3/6

40000/40000 [==============================] - 11s - loss: 0.3638 - acc: 0.8873

Epoch 4/6

40000/40000 [==============================] - 11s - loss: 0.3223 - acc: 0.8995

Epoch 5/6

40000/40000 [==============================] - 11s - loss: 0.2994 - acc: 0.9073

Epoch 6/6

40000/40000 [==============================] - 11s - loss: 0.2823 - acc: 0.9135

20000/20000 [==============================] - 2s

Epoch 1/6

40000/40000 [==============================] - 12s - loss: 0.7588 - acc: 0.7513

Epoch 2/6

40000/40000 [==============================] - 11s - loss: 0.4568 - acc: 0.8570

Epoch 3/6

40000/40000 [==============================] - 12s - loss: 0.3757 - acc: 0.8819

Epoch 4/6

40000/40000 [==============================] - 11s - loss: 0.3256 - acc: 0.8969

Epoch 5/6

40000/40000 [==============================] - 11s - loss: 0.2996 - acc: 0.9060

Epoch 6/6

40000/40000 [==============================] - 11s - loss: 0.2702 - acc: 0.9146

19904/20000 [============================>.] - ETA: 0sEpoch 1/6

40000/40000 [==============================] - 12s - loss: 0.7798 - acc: 0.7464

Epoch 2/6

40000/40000 [==============================] - 11s - loss: 0.4625 - acc: 0.8571

Epoch 3/6

40000/40000 [==============================] - 11s - loss: 0.3869 - acc: 0.8814

Epoch 4/6

40000/40000 [==============================] - 11s - loss: 0.3429 - acc: 0.8959

Epoch 5/6

40000/40000 [==============================] - 11s - loss: 0.3143 - acc: 0.9035

Epoch 6/6

40000/40000 [==============================] - 11s - loss: 0.2889 - acc: 0.9122

19840/20000 [============================>.] - ETA: 0sEpoch 1/3

40000/40000 [==============================] - 13s - loss: 0.5828 - acc: 0.8161

Epoch 2/3

40000/40000 [==============================] - 11s - loss: 0.3009 - acc: 0.9099

Epoch 3/3

40000/40000 [==============================] - 11s - loss: 0.2393 - acc: 0.9291

19680/20000 [============================>.] - ETA: 0sEpoch 1/3

40000/40000 [==============================] - 12s - loss: 0.5584 - acc: 0.8246

Epoch 2/3

40000/40000 [==============================] - 12s - loss: 0.2862 - acc: 0.9152

Epoch 3/3

40000/40000 [==============================] - 11s - loss: 0.2334 - acc: 0.9319

19488/20000 [============================>.] - ETA: 0sEpoch 1/3

40000/40000 [==============================] - 13s - loss: 0.6253 - acc: 0.8020

Epoch 2/3

40000/40000 [==============================] - 11s - loss: 0.3054 - acc: 0.9093

Epoch 3/3

40000/40000 [==============================] - 12s - loss: 0.2463 - acc: 0.9278

19808/20000 [============================>.] - ETA: 0sEpoch 1/6

40000/40000 [==============================] - 13s - loss: 0.5753 - acc: 0.8200

Epoch 2/6

40000/40000 [==============================] - 12s - loss: 0.2827 - acc: 0.9170

Epoch 3/6

40000/40000 [==============================] - 11s - loss: 0.2217 - acc: 0.9339

Epoch 4/6

40000/40000 [==============================] - 11s - loss: 0.1863 - acc: 0.9455

Epoch 5/6

40000/40000 [==============================] - 12s - loss: 0.1663 - acc: 0.9516

Epoch 6/6

40000/40000 [==============================] - 12s - loss: 0.1535 - acc: 0.9550

19680/20000 [============================>.] - ETA: 0sEpoch 1/6

40000/40000 [==============================] - 13s - loss: 0.5670 - acc: 0.8247

Epoch 2/6

40000/40000 [==============================] - 12s - loss: 0.2728 - acc: 0.9204

Epoch 3/6

40000/40000 [==============================] - 12s - loss: 0.2134 - acc: 0.9383

Epoch 4/6

40000/40000 [==============================] - 12s - loss: 0.1890 - acc: 0.9459

Epoch 5/6

40000/40000 [==============================] - 12s - loss: 0.1695 - acc: 0.9501

Epoch 6/6

40000/40000 [==============================] - 12s - loss: 0.1570 - acc: 0.9535

19712/20000 [============================>.] - ETA: 0sEpoch 1/6

40000/40000 [==============================] - 13s - loss: 0.6227 - acc: 0.7986

Epoch 2/6

40000/40000 [==============================] - 12s - loss: 0.3322 - acc: 0.9007

Epoch 3/6

40000/40000 [==============================] - 12s - loss: 0.2469 - acc: 0.9258

Epoch 4/6

40000/40000 [==============================] - 12s - loss: 0.2029 - acc: 0.9409

Epoch 5/6

40000/40000 [==============================] - 12s - loss: 0.1748 - acc: 0.9496

Epoch 6/6

40000/40000 [==============================] - 12s - loss: 0.1558 - acc: 0.9542

19872/20000 [============================>.] - ETA: 0sEpoch 1/6

60000/60000 [==============================] - 19s - loss: 0.4922 - acc: 0.8482

Epoch 2/6

60000/60000 [==============================] - 24s - loss: 0.2342 - acc: 0.9318

Epoch 3/6

60000/60000 [==============================] - 24s - loss: 0.1843 - acc: 0.9485

Epoch 4/6

60000/60000 [==============================] - 25s - loss: 0.1556 - acc: 0.9549

Epoch 5/6

60000/60000 [==============================] - 24s - loss: 0.1450 - acc: 0.9581

Epoch 6/6

60000/60000 [==============================] - 25s - loss: 0.1312 - acc: 0.9624

Yhe parameters of the best model are:

{'nb_conv': 3, 'nb_epoch': 6, 'nb_pool': 2, 'dense_layer_sizes': [64, 64], 'nb_filters': 8}


# validator.best_estimator_返回sklearn-warpped版本的最佳模型

# validator.best_estimator_.model返回未包装的最佳模型

best_model = validator.best_estimator_.model

# 度量值的名称

metric_names = best_model.metrics_names

# metric_names = ['loss', 'acc']

# 度量值的数值

metric_values = best_model.evaluate(X_test, y_test)

# metric_values = [0.0550, 0.9826]

print()

for metric, value in zip(metric_names, metric_values):

    print(metric, ': ', value)


 9984/10000 [============================>.] - ETA: 0s

loss :  0.0550105490824

acc :  0.9826

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