# for dense_model ssta and ha
predict_data = np.empty([1, 20, 27, 2])
data_y = np.empty([1, 20, 27, 2])
if is_seasonal_circle:
data_sc = np.empty([1, 1], dtype='int32')
if model_type == 'conv':
data_x = np.empty([1, 20, 27, 2])
else:
data_x = np.empty([1, 1080])
nino34 = []
for start_month in range(
file_num - prediction_month * directly_month,
file_num + month - prediction_month * directly_month + 1, interval):
predict_data[0] = file_helper_unformatted.read_data_sstaha(start_month)
if is_retrain:
predict_data = file_helper_unformatted.exchange_rows(predict_data)
# data preprocess z-zero
if data_preprocess_method == 'preprocess_Z':
predict_data = data_preprocess.preprocess_Z(predict_data, 0)
# data preprocess dimensionless
if data_preprocess_method == 'dimensionless':
redict_data = data_preprocess.dimensionless(predict_data, 0)
# data preprocess 0-1
if data_preprocess_method == 'preprocess_01':
predict_data = data_preprocess.preprocess_01(predict_data, 0)
# data preprocess no month mean
if data_preprocess_method == 'nomonthmean':
predict_data = data_preprocess.no_month_mean(predict_data, 0)
if model_type == 'conv':
data_x[0] = predict_data[0]
custom_objects={
'mean_squared_error': mean_squared_error,
'root_mean_squared_error': root_mean_squared_error,
'mean_absolute_error': mean_absolute_error
})
print(len(model.layers))
training_start = 0
all_num = 464
batch_size = 32
epochs = 200
data_preprocess_method = name_list.data_preprocess_method
all_data, testing_data = file_helper_unformatted.load_sstha_for_conv2d(
training_start, all_num)
if is_retrain:
all_data = file_helper_unformatted.exchange_rows(all_data)
if is_seasonal_circle:
sc = np.linspace(0, 11, 12, dtype='int32')
sc = np.tile(sc, int((all_num - training_start) / 12 + 1))
data_sc = sc[:(all_num - training_start)]
# data preprocess z-zero
if data_preprocess_method == 'preprocess_Z':
all_data = data_preprocess.preprocess_Z(all_data, 0)
# data preprocess dimensionless
if data_preprocess_method == 'dimensionless':
all_data = data_preprocess.dimensionless(all_data, 0)
# data preprocess 0-1
if data_preprocess_method == 'preprocess_01':
all_data = data_preprocess.preprocess_01(all_data, 0)
# data preprocess no month mean
def calculate_12_month(start_month):
# for dense_model ssta and ha
predict_data = np.empty([1, 20, 27, 2])
data_y = np.empty([1, 20, 27, 2])
data_realistic = np.empty([1, 20, 27, 2])
if is_seasonal_circle:
data_sc = np.empty([1, 1], dtype='int32')
if model_type == 'conv':
data_x = np.empty([1, 20, 27, 2])
else:
data_x = np.empty([1, 1080])
predict_data[0] = file_helper_unformatted.read_data_sstaha(start_month)
if is_retrain:
predict_data = file_helper_unformatted.exchange_rows(predict_data)
# data preprocess z-zero
if data_preprocess_method == 'preprocess_Z':
predict_data = data_preprocess.preprocess_Z(predict_data, 0)
# data preprocess dimensionless
if data_preprocess_method == 'dimensionless':
redict_data = data_preprocess.dimensionless(predict_data, 0)
# data preprocess 0-1
if data_preprocess_method == 'preprocess_01':
predict_data = data_preprocess.preprocess_01(predict_data, 0)
# data preprocess no month mean
if data_preprocess_method == 'nomonthmean':
predict_data = data_preprocess.no_month_mean(predict_data, 0)
if model_type == 'conv':
data_x[0] = predict_data[0]
else:
data_x[0] = np.reshape(predict_data[0], (1, 1080))
ssta_error = np.empty([directly_month])
for i in range(directly_month):
if is_seasonal_circle:
data_sc[0] = [(start_month + i) % 12]
data_x = model.predict([data_x, data_sc])
else:
data_x = model.predict(data_x)
if model_type == 'conv':
data_y[0] = data_x[0]
else:
data_y[0] = np.reshape(data_x[0], (20, 27, 2))
# data preprocess z-zero
if data_preprocess_method == 'preprocess_Z':
data_y = data_preprocess.preprocess_Z(data_y, 1)
# data preprocess dimensionless
if data_preprocess_method == 'dimensionless':
data_y = data_preprocess.dimensionless(data_y, 1)
# data preprocess 0-1
if data_preprocess_method == 'preprocess_01':
data_y = data_preprocess.preprocess_01(data_y, 1)
# data preprocess no month mean
if data_preprocess_method == 'nomonthmean':
data_y = data_preprocess.no_month_mean(data_y, 1)
data_realistic[0] = file_helper_unformatted.read_data_sstaha(
start_month + i + 1)
if is_retrain:
data_realistic = file_helper_unformatted.exchange_rows(
data_realistic)
# ssta_error[i] = calculate_error(data_y[0], data_realistic[0])
ssta_error[i] = math_tool.calculate_rmse(data_y[0], data_realistic[0])
print(ssta_error)
ssta_error2 = ssta_error.copy()
for i in range(1, directly_month):
ssta_error2[i] = (ssta_error[i] - ssta_error[i - 1]) / 10000
ssta_error2[0] = ssta_error2[0] / 10000
return ssta_error2
#
print(len(model.layers))
training_start = 0
training_num = 464
batch_size = 32
epochs = 2000
#
data_preprocess_method = name_list.data_preprocess_method
all_data, testing_data = file_helper_unformatted.load_sstha_for_conv2d(training_start, training_num)
all_data = data_preprocess.data_preprocess(all_data, 0, data_preprocess_method)
data_x, data_y = data_preprocess.sequence_data(all_data, input_length=time_step,
prediction_month=prediction_month)
if is_retrain:
data_x = file_helper_unformatted.exchange_rows(data_x)
data_y_nino = index_calculation.get_nino34_from_data_y(data_y)
# print(data_y_nino)
sc = np.linspace(0, 11, 12, dtype='int32')
sc = np.tile(sc, int((training_num - training_start) / 12 + 1))
data_sc = sc[:(training_num - training_start - time_step + 1 - prediction_month + 1)]
# all_data, testing_data = file_helper_unformatted.load_sstha_for_conv2d(training_start, all_num)
# for layer in model.layers[:]:
# layer.trainable = False
# for layer in model.layers[6:20]:
# layer.trainable = True
for layer in model.layers:
print(layer.trainable)
adam = optimizers.Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, amsgrad=False)
# adam = optimizers.Adam(lr=0.000005, beta_1=0.9, beta_2=0.999, epsilon=None, decay=1e-6, amsgrad=False)
# for dense_model
# predict_data = np.empty([1, 540])
# predict_data[0] = file_helper.read_data(file_num)
# file_helper.write_data(file_num, model.predict(predict_data)[0])
# for dense_model ssta and ha
predict_data = np.empty([1, 20, 27, 2])
data_y = np.empty([1, 20, 27, 2])
data_realistic = np.empty([1, 20, 27, 2])
if model_type == 'conv':
data_x = np.empty([1, 20, 27, 2])
else:
data_x = np.empty([1, 1080])
predict_data[0] = file_helper_unformatted.read_data_sstaha(file_num)
if is_retrain:
predict_data = file_helper_unformatted.exchange_rows(predict_data)
# data preprocess z-zero
if data_preprocess_method == 'preprocess_Z':
predict_data = data_preprocess.preprocess_Z(predict_data, 0)
# data preprocess dimensionless
if data_preprocess_method == 'dimensionless':
redict_data = data_preprocess.dimensionless(predict_data, 0)
# data preprocess 0-1
if data_preprocess_method == 'preprocess_01':
predict_data = data_preprocess.preprocess_01(predict_data, 0)
# data preprocess no month mean
if data_preprocess_method == 'nomonthmean':
predict_data = data_preprocess.no_month_mean(predict_data, 0)
if model_type == 'conv':