CLF1_TEST.append(
np.dstack((each_emb[0].reshape((10, 8000)), each_emb[1].reshape(
(10, 8000)), each_emb[2].reshape(
(10, 8000)), each_emb[3].reshape((10, 8000)))))
#except any error then remove that file manually and then run the process again
except OSError:
print 'Test Pickling Error: ', each_wav
#################################################################################
# Preprocess the data as per the input of the model
#################################################################################
CLF1_TEST = np.array(CLF1_TEST).reshape((-1, 10, 8000, 4))
CLF1_TEST = CLF1_TEST / np.linalg.norm(CLF1_TEST)
#################################################################################
# Run the predictions on the data
#################################################################################
PREDICTIONS = MODEL.predict(CLF1_TEST).ravel()
DF_TEST['predictions_prob'] = PREDICTIONS
DF_TEST['predictions'] = PREDICTIONS.ravel().round()
#################################################################################
# save it in a CSV file
#################################################################################
if RESULT.path_to_write_prediction_csv:
DF_TEST.drop(["features"],
axis=1).to_csv(RESULT.path_to_write_prediction_csv)
else:
print "Predictions are not saved. Give appropiate argument to save the predictions"
strides=100,
activation='relu',
padding='same'),
input_shape=(10, 8000, 4))(INPUTS)
CONV_2 = TimeDistributed(
Conv1D(100, kernel_size=4, activation='relu', padding='same'))(CONV_1)
MAX_POOL = TimeDistributed(MaxPooling1D(80))(CONV_2)
DENSE_1 = TimeDistributed(Dense(60, activation='relu'))(MAX_POOL)
DENSE_2 = TimeDistributed(Dense(50, activation='relu'))(DENSE_1)
DENSE_3 = TimeDistributed(Dense(1, activation='sigmoid'))(DENSE_2)
MAX_POOL_2 = MaxPooling2D((10, 1))(DENSE_3)
PREDICTIONS = Flatten()(MAX_POOL_2)
MODEL = Model(inputs=[INPUTS], outputs=[PREDICTIONS])
print MODEL.summary()
# Load the saved weights and predict on the audiomoth recordings
MODEL.load_weights('../Goertzel_model_8k_weights_time.h5')
#call the audiomoth function to predict on the frequency components of audio moth WAV files
TEST_VALUES, DATAFRAME = aud_goertzel.dataframe_with_frequency_components(
RESULT.audio_files_path, RESULT.path_for_goertzel_components)
PREDICTIONS = MODEL.predict(TEST_VALUES).ravel()
DATAFRAME['predictions_prob'] = PREDICTIONS
DATAFRAME['predictions'] = PREDICTIONS.ravel().round()
#save it in a CSV file
DATAFRAME[[
'wav_file', 'Label_1', 'Label_2', 'Label_3', 'predictions_prob',
'predictions'
]].to_csv('predictions_by_goertzel_model.csv')
