官网实例详解4.31（mnist_sklearn_wrapper.py）-keras学习笔记四

使用sklearn包装器实例（重点：sklearn的GridSearchCV方法如何使用）

Builds simple CNN models on MNIST and uses sklearn's GridSearchCV to find best model
基于MNIST（数据集）建立简单CNN（卷积神经网络）模型，使用sklearn的GridSearchCV方法找出最佳模型

GridSearchCV，意义是自动调参，参数输进去，就能给出最优化的结果和参数。但适合于小数据集，数据的量级大了，很难得出结果。
官网介绍

Keras实例目录

代码注释

'''Example of how to use sklearn wrapper
使用sklearn包装器实例（重点：sklearn的GridSearchCV方法如何使用）
Builds simple CNN models on MNIST and uses sklearn's GridSearchCV to find best model
基于MNIST（数据集）建立简单CNN（卷积神经网络）模型，使用sklearn的GridSearchCV方法找出最佳模型


GridSearchCV，它存在的意义就是自动调参，只要把参数输进去，就能给出最优化的结果和参数。但是这个方法适合于小数据集，
数据的量级上去了，很难得出结果。
http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html

'''

from __future__ import print_function

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.wrappers.scikit_learn import KerasClassifier
from keras import backend as K
from sklearn.grid_search import GridSearchCV


num_classes = 10

# input image dimensions
# 输入图像维度
img_rows, img_cols = 28, 28

# load training data and do basic data normalization
# 加载训练集数据并做基础的数据归一化处理
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':# Theano框架，图像通道在前
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else: # TensorFlow框架，图像通道在后
    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)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

# convert class vectors to binary class matrices
# 类别向量转为多分类矩阵
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)


def make_model(dense_layer_sizes, filters, kernel_size, pool_size):
    '''Creates model comprised of 2 convolutional layers followed by dense layers
    创建模型组成：2个卷积层后跟一个全连接层

    dense_layer_sizes: List of layer sizes.
        This list has one number for each layer
    dense_layer_sizes: 层大小列表
        列表每个层都有一个数字
    filters: Number of convolutional filters in each convolutional layer
    filters: 每个卷积层的卷积核数量
    kernel_size: Convolutional kernel size
    kernel_size: 卷积核大小
    pool_size: Size of pooling area for max pooling
    pool_size: 最大池化的池化区域大小
    '''

    model = Sequential()
    model.add(Conv2D(filters, kernel_size,
                     padding='valid',
                     input_shape=input_shape))
    model.add(Activation('relu'))
    model.add(Conv2D(filters, kernel_size))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=pool_size))
    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(num_classes))
    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy',
                  optimizer='adadelta',
                  metrics=['accuracy'])

    return model

dense_size_candidates = [[32], [64], [32, 32], [64, 64]]
my_classifier = KerasClassifier(make_model, batch_size=32)
validator = GridSearchCV(my_classifier,
                         param_grid={'dense_layer_sizes': dense_size_candidates,
                                     # epochs is avail for tuning even when not
                                     # an argument to model building function
                                     # 即使在没有建立模型功能的参数时，也有必要进行调整。
                                     'epochs': [3, 6],
                                     'filters': [8],
                                     'kernel_size': [3],
                                     'pool_size': [2]},
                         scoring='neg_log_loss',
                         n_jobs=1)
validator.fit(x_train, y_train)

print('The parameters of the best model are: ')
print(validator.best_params_)

# validator.best_estimator_ returns sklearn-wrapped version of best model.
# validator.best_estimator_返回sklearn-wrapped版本的最佳模型。
# validator.best_estimator_.model returns the (unwrapped) keras model
# validator.best_estimator_.model 返回（展开的）keras模型
best_model = validator.best_estimator_.model
metric_names = best_model.metrics_names
metric_values = best_model.evaluate(x_test, y_test)
for metric, value in zip(metric_names, metric_values):
    print(metric, ': ', value)

代码执行

 

Keras详细介绍

英文：https://keras.io/

中文：http://keras-cn.readthedocs.io/en/latest/

实例下载

https://github.com/keras-team/keras

https://github.com/keras-team/keras/tree/master/examples

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