return model
text_class_model = build_model(113)
earlyStopping = keras.callbacks.EarlyStopping(monitor='loss',
patience=10,
verbose=1,
mode='auto')
text_class_model.fit(train_data,
train_labels,
epochs=50,
batch_size=5,
callbacks=[earlyStopping])
ml_utils.save_model(text_class_model, 'text_class_model.h5')
test_predicted_res = text_class_model.predict(test_data, batch_size=1)
print(
'\n****************Classification result for text classification************************'
)
ml_utils.display_result(
test_labels_raw, test_predicted_res.argmax(axis=1),
'text classification') # Print the classification result
# for result in test_predicted_res:
# ml_utils.display_confidence(result)
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())
model.add(Dense(128))
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')
model.fit(train,
train_label,
batch_size=batch_size,
nb_epoch=nb_epoch,
verbose=1,
validation_data=(validation, validation_label))
logger.info("model training complete")
score = model.evaluate(validation, validation_label, verbose=0)
logger.info("validation score: %f" % (score))
save_model(model)
logger.info("model saved")
model = Sequential()
model.add(Convolution2D(nb_filters, nb_conv, nb_conv, border_mode = 'valid', input_shape = (1, img_rows, img_cols)))
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())
model.add(Dense(128))
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')
model.fit(train, train_label, batch_size = batch_size, nb_epoch = nb_epoch, verbose = 1, validation_data = (validation, validation_label))
logger.info("model training complete")
score = model.evaluate(validation, validation_label, verbose = 0)
logger.info("validation score: %f" % (score))
save_model(model)
logger.info("model saved")
earlyStopping = keras.callbacks.EarlyStopping(monitor='val_loss',
patience=10,
verbose=1,
mode='auto')
model_rack = build_model(5)
history_rack = model_rack.fit(train_rack_data,
train_rack_labels,
epochs=10,
validation_data=(test_rack_data,
test_rack_labels),
batch_size=500,
verbose=2,
callbacks=[earlyStopping])
ml_utils.save_model(model_rack, 'model_rack.h5')
model_container = build_model(25)
start_time = time.time()
history_container = model_container.fit(
train_container_data,
train_container_labels,
epochs=50,
validation_data=(test_container_data, test_container_labels),
batch_size=1000,
verbose=2,
callbacks=[earlyStopping])
elapsed_time = time.time() - start_time
print('Deep Learning Training time: {}'.format(elapsed_time))
ml_utils.save_model(model_container, 'model_container.h5')
epochs=20,
validation_data=(test_left_data,
test_left_labels),
batch_size=500,
verbose=2)
model_right = dl_clf.build_model(8, input_shape)
history_right = model_right.fit(train_right_data,
train_right_labels,
epochs=20,
validation_data=(test_right_data,
test_right_labels),
batch_size=500,
verbose=2)
ml_utils.save_model(model_bottom, 'model_bottom.h5',
config_save_load_dir_path)
ml_utils.save_model(model_left, 'model_left.h5', config_save_load_dir_path)
ml_utils.save_model(model_right, 'model_right.h5',
config_save_load_dir_path)
test_predicted_bottom_res = model_bottom.predict(test_bottom_data,
batch_size=1)
print(
'\n****************Classification result for Bottom************************'
)
ml_utils.display_result(test_bottom_labels_raw,
test_predicted_bottom_res.argmax(axis=1),
'Bottom') # Print the classification result
# for result in test_predicted_bottom_res:
# ml_utils.display_confidence(result)
# earlyStopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, verbose=1, mode='auto')
model_bottom = dl_clf.build_model(5, 6)
history_bottom = model_bottom.fit(train_bottom_data, train_bottom_labels, epochs=10,
validation_data=(test_bottom_data, test_bottom_labels), batch_size=500, verbose=2)
model_left = dl_clf.build_model(7, 6)
history_left = model_left.fit(train_left_data, train_left_labels, epochs=10,
validation_data=(test_left_data, test_left_labels), batch_size=500, verbose=2)
model_right = dl_clf.build_model(8, 6)
history_right = model_right.fit(train_right_data, train_right_labels, epochs=10,
validation_data=(test_right_data, test_right_labels), batch_size=500, verbose=2)
ml_utils.save_model(model_bottom, 'model_bottom.h5')
ml_utils.save_model(model_left, 'model_left.h5')
ml_utils.save_model(model_right, 'model_right.h5')
test_predicted_bottom_res = model_bottom.predict(test_bottom_data, batch_size=1)
print('\n****************Classification result for Bottom************************')
ml_utils.display_result(test_bottom_labels_raw, test_predicted_bottom_res.argmax(axis=1),
'Bottom') # Print the classification result
# for result in test_predicted_bottom_res:
# ml_utils.display_confidence(result)
test_predicted_left_res = model_left.predict(test_left_data, batch_size=1)
print('\n****************Classification result for Left************************')
ml_utils.display_result(test_left_labels_raw, test_predicted_left_res.argmax(axis=1),
'Left') # Print the classification result