Keras RMSE MAE finding - tensorflow

Question

I have a problem with my code. I will be very happy if you can help.
The purpose is having mean absolute errors and root mean square errors with different epochs and batch sizes. I'm very new in deep learning so I have tried to do that like this. However, i am very confused.
How can I fix or rewrite this code. Thank you so much.
# Reading the file
df = pd.read_csv('data.csv')
df = df[df.columns.difference(['Unnamed: 0'])]
input_data = df.iloc[:,:100].values
label_MOS = df['MOS'].values
train_X, val_X, train_y, val_y = train_test_split(input_data,
label_MOS, test_size = 0.25, random_state = 14)
x_train = train_X
y_train = train_y
x_test = val_X
y_test = val_y
def create_model():
model=Sequential()
model.add(Dense(32, input_dim=100, kernel_initializer='normal', activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
adam=Adam(learning_rate=0.1)
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mae'])
return model
# Create the model
model = KerasClassifier(build_fn = create_model,verbose = 0)
# Define the grid search parameters
batch_size = [20]
epochs = [500,1000]
# Make a dictionary of the grid search parameters
param_grid = dict(batch_size = batch_size,epochs = epochs)
# Build and fit the GridSearchCV
grid = GridSearchCV(estimator = model,param_grid = param_grid,cv = KFold(),verbose = )
grid_result = grid.fit(x_train,y_train)
NNpredictions = model.predict(x_test)
MAE = mean_absolute_error(val_y , NNpredictions)
RMSE = mean_squared_error(val_y , NNpredictions, squared = False)
# Summarize the results
print(' MAE {}, RMSE {}'.format(MAE.best_score_,RMSE.best_params_))
mae = MAE.cv_results_['mae']
rmse = RMSE.cv_results_['rmse']
# params = grid_result.cv_results_['params']
for mean, stdev in zip(mae, rmse):
print("mae %f rmse (%f) " % (mean, stdev))

Answer 1

I would do something like this:
batch_size = [20]
epochs = [500,1000]
result_list = list()
for batch_value in batch_size:
for epoch_value in epochs:
model = create_model()
model.fit(x=x_train,y=y_train,epochs=epoch_value, batch_size=batch_value)
metrics = model.evaluate(x=x_test,y=y_test)
ord_dic = collections.OrderedDict()
ord_dic['batch_size'] = batch_value
ord_dic['epochs'] = epoch_value
ord_dic['metrics'] = metrics
result_list.append(ord_dic)
print(result_list)
I have put the results in a list of Ordered Dictionaries, but you can easily change that part

Answer 2

I updated your code
from keras import backend as K
def create_model(losses='mse'):
model=Sequential()
model.add(Dense(32, input_dim=100, kernel_initializer='normal', activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
adam=Adam(learning_rate=0.1)
model.compile(optimizer=adam, loss=losses, metrics=['accuracy'])
return model
def root_mean_squared_error(y_true, y_pred):
return K.sqrt(K.mean(K.square(y_pred - y_true)))
batch_size = [20]
epochs = [500,1000]
losses = ['mse', root_mean_squared_error]
neural_network = KerasClassifier(build_fn=network, verbose = 1)
param_grid = dict(losses=losses, epochs=epochs, batch_size = batches)
grid = GridSearchCV(estimator=neural_network, param_grid=param_grid )
grid_result = grid.fit(X_train, y_train)
print(grid_result.best_params_)
The create_model function needs to have a losses parameter, it's where your grid will pass the parameter.