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cnn-basic.py
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cnn-basic.py
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#!/usr/bin/env python
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
from keras.models import load_model
import data_reader as reader
np.random.seed(1337)
batch_size = 128
nb_classes = 10
nb_epoch = 20
# input image dimensions
img_rows, img_cols = 28, 28
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
pool_size = (2, 2)
# convolution kernel size
kernel_size = (3, 3)
def my_model(input_shape):
model = Sequential()
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1], border_mode='valid', input_shape=input_shape))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.summary()
return model
def save_model(model,path):
model.save(path)
def load_model(path):
model=load_weights(path)
return model
def train():
X_train, Y_train, X_test, Y_test = get_data()
input_shape=(img_rows,img_cols,1)
model=my_model(input_shape) # get model
model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'])
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,verbose=1, validation_data=(X_test, Y_test))
save_model(model,'pretrained/cnn-basic-model.h5')
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
def get_data():
data_x = reader.read_data("pickle/img_data.pickle")
data_y = reader.read_data("pickle/img_label.pickle")
tr_lim = int(len(data_x) * 70 / 100)
X_train, Y_train = data_x[:tr_lim], data_y[:tr_lim]
X_test, Y_test = data_x[tr_lim:], data_y[tr_lim:]
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')
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
return X_train, Y_train, X_test, Y_test
def print_data(data):
for x in data:
print len(x), len(x[0])
print "==" * 50
def main():
train()
if __name__ == "__main__":
main()