75 lines
2.2 KiB
Python
75 lines
2.2 KiB
Python
"""
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related to CNN_detect 7,8
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"""
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Flatten, Activation
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from keras.layers import Convolution2D, MaxPooling2D
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from keras.utils import np_utils
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from util import *
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BATCH_SIZE = 100
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NB_CLASSES = 2
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NB_EPOCH = 5
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img_rows, img_cols = (50, 50)
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INPUT_SHAPE = (img_rows, img_cols, 1)
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MODEL_SAVE_PATH = './CNN_model9.h5'
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print("loading data...")
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X, y = load_data(['./data/positive_data3.csv', './data/negative_not_empty4.csv'])
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train_size = int(0.9 * X.shape[0])
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X_train = X[0:train_size, :]
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y_train = y[0:train_size]
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X_test = X[train_size:, :]
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y_test = y[train_size:]
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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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X_train = X_train.transpose(0, 2, 3, 1)
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X_test = X_test.transpose(0, 2, 3, 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 /= 65535.
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X_test /= 65535.
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print('X_train shape:', X_train.shape)
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print(X_train.shape[0], 'train samples')
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print(X_test.shape[0], 'test samples')
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Y_train = np_utils.to_categorical(y_train, NB_CLASSES)
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Y_test = np_utils.to_categorical(y_test, NB_CLASSES)
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# define the CNN
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model = Sequential()
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model.add(Convolution2D(nb_filter=16, nb_row=3, nb_col=3, border_mode='valid', input_shape=INPUT_SHAPE))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Convolution2D(nb_filter=32, nb_row=3, nb_col=3, border_mode='same'))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Convolution2D(nb_filter=32, nb_row=3, nb_col=3, border_mode='same'))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Flatten())
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model.add(Dense(32))
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model.add(Activation('relu'))
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model.add(Dropout(0.5))
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model.add(Dense(NB_CLASSES))
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model.add(Activation('softmax'))
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model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
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model.fit(X_train, Y_train, batch_size=BATCH_SIZE, nb_epoch=NB_EPOCH, shuffle=True,
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verbose=1, validation_split=0.1)
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model.save(MODEL_SAVE_PATH)
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"""
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model = load_model(MODEL_SAVE_PATH)
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"""
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score = model.evaluate(X_test, Y_test, verbose=0)
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print('Test score:', score[0])
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print('Test accuracy:', score[1])
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