optimizer=opt,
# loss=c_abs_metric,
# metrics=[c_abs_metric,mean_absolute_error]
loss=loss,
metrics=[mean_absolute_error])
early_stopping_monitor = EarlyStopping(patience=1)
# checkpoint = ModelCheckpoint('./checkpoints/mlp.mdl',save_best_only=False)
model.fit(
x_train,
y_train,
batch_size=1,
epochs=25,
verbose=1,
validation_data=(x_valid, y_valid),
callbacks=[
early_stopping_monitor,
# checkpoint
])
score = model.evaluate(x_test, y_test, batch_size=1, verbose=0)
print(brand)
print(loss)
print(opt)
print(score)
models[brand] = model
except Exception as e:
print(e)
# It's a simple report generator
genRep = ReportGenerator('./output/' + brand + '/', y_test, x_test,
scaler_y)
genRep.generate(models)
def main(report_file, unique_id):
r = ReportGenerator(ReportFile=report_file, UniqueId=unique_id)
r.import_data()
r.process_data()
r.generate()