103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
'''Example of how to use sklearn wrapper
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Builds simple CNN models on MNIST and uses sklearn's GridSearchCV to find best model
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'''
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from __future__ import print_function
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import keras
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from keras.datasets import mnist
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Activation, Flatten
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from keras.layers import Conv2D, MaxPooling2D
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from keras.wrappers.scikit_learn import KerasClassifier
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from keras import backend as K
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from sklearn.grid_search import GridSearchCV
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num_classes = 10
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# input image dimensions
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img_rows, img_cols = 28, 28
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# load training data and do basic data normalization
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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if K.image_data_format() == 'channels_first':
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x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
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x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
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input_shape = (1, img_rows, img_cols)
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else:
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x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
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x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
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input_shape = (img_rows, img_cols, 1)
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x_train = x_train.astype('float32')
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x_test = x_test.astype('float32')
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x_train /= 255
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x_test /= 255
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# convert class vectors to binary class matrices
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y_train = keras.utils.to_categorical(y_train, num_classes)
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y_test = keras.utils.to_categorical(y_test, num_classes)
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def make_model(dense_layer_sizes, filters, kernel_size, pool_size):
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'''Creates model comprised of 2 convolutional layers followed by dense layers
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dense_layer_sizes: List of layer sizes.
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This list has one number for each layer
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filters: Number of convolutional filters in each convolutional layer
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kernel_size: Convolutional kernel size
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pool_size: Size of pooling area for max pooling
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'''
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model = Sequential()
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model.add(Conv2D(filters, kernel_size,
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padding='valid',
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input_shape=input_shape))
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model.add(Activation('relu'))
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model.add(Conv2D(filters, kernel_size))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=pool_size))
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model.add(Dropout(0.25))
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model.add(Flatten())
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for layer_size in dense_layer_sizes:
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model.add(Dense(layer_size))
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model.add(Activation('relu'))
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model.add(Dropout(0.5))
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model.add(Dense(num_classes))
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model.add(Activation('softmax'))
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model.compile(loss='categorical_crossentropy',
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optimizer='adadelta',
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metrics=['accuracy'])
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return model
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dense_size_candidates = [[32], [64], [32, 32], [64, 64]]
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my_classifier = KerasClassifier(make_model, batch_size=32)
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validator = GridSearchCV(my_classifier,
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param_grid={'dense_layer_sizes': dense_size_candidates,
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# epochs is avail for tuning even when not
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# an argument to model building function
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'epochs': [3, 6],
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'filters': [8],
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'kernel_size': [3],
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'pool_size': [2]},
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scoring='neg_log_loss',
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n_jobs=1)
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validator.fit(x_train, y_train)
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print('The parameters of the best model are: ')
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print(validator.best_params_)
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# validator.best_estimator_ returns sklearn-wrapped version of best model.
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# validator.best_estimator_.model returns the (unwrapped) keras model
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best_model = validator.best_estimator_.model
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metric_names = best_model.metrics_names
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metric_values = best_model.evaluate(x_test, y_test)
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for metric, value in zip(metric_names, metric_values):
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print(metric, ': ', value)
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