# Split data in to features and target
x_train = features.values
y_train = target.values
# Apply min max normalization
scaler = MinMaxScaler().fit(x_train)
x_train = scaler.transform(x_train)
# Find best parameters
grid_search.fit(x_train, y_train)
print(grid_search.best_params_)
print(grid_search.best_score_)
# Retrain model with best parameters found from grid search
best_params = grid_search.best_params_
model = model2(input_dim,
loss=best_params['loss'],
r1=best_params['r1'],
l1=best_params['l1'],
r2=best_params['r2'],
l2=best_params['l2'])
model.fit(x_train,
y_train,
epochs=best_params['epochs'],
batch_size=best_params['batch_size'],
verbose=1)
# save the model
filename = '../models/final_ANN_model.h5'
model.save(filename)
callbacks=[early_stopping, tensorboard])
fit_models.append(model)
predictions = [
dataload.predict_sequences_multiple(model, X_test, seq_len,
predict_len)
for model in top_models
]
scores = [
model.evaluate(X_test, y_test, verbose=0) for model in top_models
]
# Save results
os.makedirs(results_fname)
folder_name = 'seq_len_{}'.format(seq_len)
os.makedirs('{}/{}'.format(results_fname, folder_name))
results.to_csv('{0}/{1}/results.csv'.format(results_fname,
folder_name))
top_model_plots = [(predictions[i], 'Model {}'.format(i + 1))
for i in range(len(predictions))]
plot_results_multiple(top_model_plots,
y_test,
predict_len,
fig_path='{0}/{1}/plots.pdf'.format(
results_fname, folder_name))
index = 1
for model in fit_models:
model.save('{}/{}/model-{}.h5'.format(results_fname, folder_name,
index))
index = index + 1
shuffle=shuffle,
validation_split=validation_split)
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1)
grid_result = grid.fit(encoded_X_train, Y_train)
# summarize results
print("Best: %f using %s" %
(grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))
else:
model = create_model(neurons=100,
optimizer='rmsprop',
init='glorot_uniform')
# model.fit(train_data, train_label, batch_size=20, epochs=100, shuffle=True, verbose=1, validation_split=0.2)
model.fit(encoded_X_train,
Y_train,
batch_size=10,
epochs=150,
shuffle=True,
verbose=1,
validation_split=0.2)
result = model.evaluate(encoded_X_test, Y_test, batch_size=1000)
print('loss:%5.6f acct:%5.6f' % (result[0], result[1]))
# Save the trained model to disk
model.save(MODEL_FILENAME)
print(model.predict(encoded_X_train))