def hyper_tuner(month_path, log_path, time_step, train_data, valid_data,
test_data, item, item_index, item_name):
train_x, valid_x, test_x, train_y, valid_y, test_y = data_split_norm(
train_data, valid_data, test_data, item_index, time_step)
hyper_model = MyHyperModel(input_shape=train_x.shape[-2:])
project_name = item_name + "_" + str(time_step)
tuner = RandomSearch(hyper_model,
objective='val_loss',
max_trials=150,
executions_per_trial=5,
directory=log_path,
project_name=project_name)
callbacks = [
tf.keras.callbacks.EarlyStopping(monitor='val_loss',
min_delta=1e-3,
patience=15,
restore_best_weights=True),
tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
verbose=0,
mode='auto',
min_delta=0.0001),
ClearTrainingOutput()
]
tuner.search_space_summary()
tuner.search(train_x,
train_y,
epochs=200,
batch_size=256,
validation_data=(valid_x, valid_y),
callbacks=callbacks,
verbose=2)
# Get the optimal hyperparameters
best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
model = tuner.hypermodel.build(best_hps)
history = model.fit(train_x,
train_y,
epochs=200,
batch_size=256,
validation_data=(valid_x, valid_y),
callbacks=callbacks,
verbose=2)
models_path = os.path.join(month_path, "models")
if not os.path.exists(models_path):
os.mkdir(models_path)
model_name = item + "_model.h5"
model_path = os.path.join(models_path, model_name)
model.save(model_path)
# 清除tuner
del tuner
layers_path = os.path.join(month_path, item)
if not os.path.exists(layers_path):
os.mkdir(layers_path)
plot_path_1 = os.path.join(layers_path, "loss_epoch.png")
plot_path_2 = os.path.join(layers_path, "measure_predict.png")
csv_path = os.path.join(layers_path, "measure_predict.csv")
history = history.history
plt.plot(history['loss'], linewidth=2, label='Train')
plt.plot(history['val_loss'], linewidth=2, label='Test')
plt.legend(loc='upper right')
plt.ylabel('Mean Relative Error [$' + item + '$]')
plt.xlabel('Epoch')
plt.savefig(plot_path_1, dpi=300, bbox_inches="tight")
plt.close()
test_predictions = model.predict(test_x)
plt.figure(figsize=(20, 6))
plt.plot(test_y, label='measure')
plt.plot(test_predictions, label='predict')
plt.legend(loc='upper right')
if item is 'PH':
plt.ylabel(item)
else:
plt.ylabel(item + '(mg/L)')
plt.xlabel('Test set')
plt.savefig(plot_path_2, dpi=300, bbox_inches="tight")
plt.close()
test_predictions = test_predictions.flatten() # flatten()降为一维
valid_predictions = model.predict(valid_x).flatten()
train_fvu, train_rmse, train_mre = history['loss'][-1], history[
'root_mean_squared_error'][-1], history[
"mean_absolute_percentage_error"][-1]
valid_fvu, valid_rmse, valid_mre = model.evaluate(valid_x,
valid_y,
verbose=0)
valid_nse, valid_cc = calc_nse_cc(valid_y, valid_predictions)
test_fvu, test_rmse, test_mre = model.evaluate(test_x, test_y, verbose=0)
test_nse, test_cc = calc_nse_cc(test_y, test_predictions)
measure_predict_data = pd.DataFrame()
measure_predict_data['predict'] = test_predictions
measure_predict_data['measure'] = test_y
measure_predict_data.to_csv(csv_path, encoding='utf-8')
try:
hidden_layer_1 = best_hps.get('units_0')
except:
hidden_layer_1 = 0
try:
hidden_layer_2 = best_hps.get('units_1')
except:
hidden_layer_2 = 0
model_info = {
'output': item,
"time_step": time_step,
'hidden_layer_1': hidden_layer_1,
'hidden_layer_2': hidden_layer_2,
'hidden_layer_3': best_hps.get('units_n'),
'train_fvu': train_fvu,
'train_rmse': train_rmse,
'train_mre': train_mre,
"valid_fvu": valid_fvu,
'valid_nse': valid_nse,
'valid_rmse': valid_rmse,
'valid_mre': valid_mre,
'valid_cc': valid_cc,
"test_fvu": test_fvu,
'test_nse': test_nse,
'test_rmse': test_rmse,
'test_mre': test_mre,
'test_cc': test_cc
}
return model_info
def hyper_tuner(log_path, time_step, train_data, valid_data, test_data, item,
item_index, item_name):
keras.backend.clear_session()
train_x, valid_x, test_x, train_y, valid_y, test_y = data_split_norm(
train_data, valid_data, test_data, item_index, time_step)
hyper_model = MyHyperModel(input_shape=train_x.shape[-2:])
project_name = item_name + "_" + str(time_step)
tuner = RandomSearch(hyper_model,
objective='val_loss',
max_trials=150,
executions_per_trial=10,
directory=log_path,
project_name=project_name)
callbacks = [
tf.keras.callbacks.EarlyStopping(monitor='val_loss',
min_delta=1e-3,
patience=15,
restore_best_weights=True),
tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
verbose=0,
mode='auto',
min_delta=0.0001),
ClearTrainingOutput()
]
tuner.search_space_summary()
tuner.search(train_x,
train_y,
epochs=200,
batch_size=256,
validation_data=(valid_x, valid_y),
callbacks=callbacks,
verbose=2)
# Get the optimal hyperparameters
best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
try:
hidden_layer_1 = best_hps.get('units_0')
except:
hidden_layer_1 = 0
try:
hidden_layer_2 = best_hps.get('units_1')
except:
hidden_layer_2 = 0
model_info = {
'output': item,
"time_step": time_step,
'hidden_layer_1': hidden_layer_1,
'hidden_layer_2': hidden_layer_2,
'hidden_layer_3': best_hps.get('units_n'),
}
# 清除tuner
del tuner
return model_info
LOG_DIR = f"{int(time.time())}"
tuner = RandomSearch(build_model,
objective="val_accuracy",
max_trials=1,
executions_per_trial=1,
directory=LOG_DIR)
tuner.search(x=X_train,
y=y_train,
epochs=150,
batch_size=64,
validation_split=0.1)
print(tuner.get_best_hyperparameters()[0].values)
print(tuner.results_summary())
best_model = tuner.get_best_models(num_models=1)[0]
NN_pred = best_model.predict(X_test)
pred = list()
for i in range(len(NN_pred)):
pred.append(np.argmax(NN_pred[i]))
test = list()
for i in range(len(y_test)):
test.append(np.argmax(y_test[i]))
train_pred = best_model.predict(X_train)
train_prediction = list()
for i in range(len(train_pred)):
train_prediction.append(np.argmax(train_pred[i]))
train = list()
callbacks = [
tf.keras.callbacks.EarlyStopping(monitor='val_loss',
mode='min',
patience=100,
restore_best_weights=True,
min_delta=0.00001,
verbose=1)
]
tuner_rs.search(x_train_simple_net,
y_train_simple_net,
epochs=1000,
validation_split=0.2,
verbose=0,
callbacks=callbacks)
best_hp = tuner_rs.get_best_hyperparameters(num_trials=10)
model = tuner_rs.hypermodel.build(best_hp)
# run1 = {'units': 8, 'activation': 'relu', 'kernel_initializer': 'random_normal', 'bias_initializer': 'lecun_normal', 'regularizers.l2': 0.001, 'learning_rate': 0.001}
# run2 = {'units': 8, 'activation': 'elu', 'kernel_initializer': 'random_normal', 'bias_initializer': 'lecun_normal', 'regularizers.l2': 0.001, 'learning_rate': 0.001}
# run3 = {'units': 8, 'activation': 'elu', 'kernel_initializer': 'random_uniform', 'bias_initializer': 'random_normal', 'regularizers.l2': 0.001, 'learning_rate': 0.001}
# run4 = {'units': 6, 'activation': 'elu', 'kernel_initializer': 'random_normal', 'bias_initializer': 'random_uniform', 'regularizers.l2': 0.001, 'learning_rate': 0.001}
# run5 = {'units': 8, 'activation': 'elu', 'kernel_initializer': 'random_uniform', 'bias_initializer': 'lecun_normal', 'regularizers.l2': 0.001, 'learning_rate': 0.001}
best_model = tuner_rs.get_best_models(num_models=1)[0]
# loss, mse = best_model.evaluate(x_valid_simple_net, y_valid_simple_net)
# ----------------------------after finding the best hyper-parameters train the model-----------------------------
# net_model, model_history, early_stoping = obj1.tuned_net(x_train_simple_net, y_train_simple_net)
return model
tuner_search = RandomSearch(build_model,
objective='accuracy',
max_trials=5,
directory=r"C:\Users\khares\Work\output13")
tuner_search.search(training_set, epochs=5)
model = tuner_search.get_best_models(num_models=1)[0]
model.summary()
best_hyperparameters = tuner_search.get_best_hyperparameters(1)[0].values
best_hyperparameters
model = Sequential()
model.add(
Conv2D(filters=48,
kernel_size=5,
padding="same",
activation="relu",
input_shape=(224, 224, 3)))
model.add(MaxPooling2D(pool_size=4))
model.add(Conv2D(filters=160, kernel_size=3, padding="same",
activation="relu"))
model.add(MaxPooling2D(pool_size=4))
model.add(Conv2D(filters=144, kernel_size=5, padding="same",
activation="relu"))
