def callback(weights, iter):
if iter % 10 == 0:
print("max of weights", np.max(np.abs(weights)))
train_preds = undo_norm(
pred_fun(weights, train_smiles[:num_print_examples]))
cur_loss = loss_fun(weights, train_smiles[:num_print_examples],
train_targets[:num_print_examples])
training_curve.append(cur_loss)
print(
"Iteration",
iter,
"loss",
cur_loss,
"train RMSE",
rmse(train_preds, train_raw_targets[:num_print_examples]),
)
if validation_smiles is not None:
validation_preds = undo_norm(
pred_fun(weights, validation_smiles))
print(
"Validation RMSE",
iter,
":",
rmse(validation_preds, validation_raw_targets),
)
def print_performance(pred_func):
train_preds = pred_func(train_inputs)
val_preds = pred_func(val_inputs)
print("\nPerformance (RMSE) on " + task_params['target_name'] + ":")
print("Train:", rmse(train_preds, train_targets))
print("Test: ", rmse(val_preds, val_targets))
print("-" * 80)
return rmse(val_preds, val_targets)
def print_performance(pred_func):
train_preds = pred_func(train_inputs)
val_preds = pred_func(val_inputs)
print "\nPerformance (RMSE) on " + task_params['target_name'] + ":"
print "Train:", rmse(train_preds, train_targets)
print "Test: ", rmse(val_preds, val_targets)
print "-" * 80
return rmse(val_preds, val_targets)
def callback(weights, iter):
if iter % 10 == 0:
print "max of weights", np.max(np.abs(weights))
train_preds = undo_norm(pred_fun(weights, train_smiles))
cur_loss = loss_fun(weights, train_smiles, train_targets)
training_curve.append(cur_loss)
print "Iteration", iter, "loss", cur_loss, "train RMSE", rmse(train_preds, train_raw_targets),
if validation_smiles is not None:
validation_preds = undo_norm(pred_fun(weights, validation_smiles))
print "Validation RMSE", iter, ":", rmse(validation_preds, validation_raw_targets),
def callback(weights, iter):
if iter % 10 == 0:
print "max of weights", np.max(np.abs(weights))
# import pdb; pdb.set_trace()
train_preds = undo_norm(pred_fun(weights, train_smiles[:num_print_examples]))
cur_loss = loss_fun(weights, train_smiles[:num_print_examples], train_targets[:num_print_examples]) # V: refers to line number #78 i.e.
# def loss_fun(weights, smiles, targets) of build_vanilla_net.py
training_curve.append(cur_loss)
print "Iteration", iter, "loss", cur_loss,\
"train RMSE", rmse(train_preds, train_raw_targets[:num_print_examples]),
if validation_smiles is not None:
validation_preds = undo_norm(pred_fun(weights, validation_smiles))
print "Validation RMSE", iter, ":", rmse(validation_preds, validation_raw_targets),
def run_conv_experiment():
conv_layer_sizes = [model_params['conv_width']] * model_params['fp_depth']
conv_arch_params = {'num_hidden_features' : conv_layer_sizes,
'fp_length' : model_params['fp_length'], 'normalize' : 1}
loss_fun, pred_fun, conv_parser = \
build_conv_deep_net(conv_arch_params, vanilla_net_params, model_params['L2_reg'])
num_weights = len(conv_parser)
predict_func, trained_weights, conv_training_curve = \
train_nn(pred_fun, loss_fun, num_weights, train_inputs, train_targets,
train_params, validation_smiles=val_inputs, validation_raw_targets=val_targets)
test_predictions = predict_func(test_inputs)
return rmse(test_predictions, test_targets)
def run_conv_experiment():
conv_layer_sizes = [model_params['conv_width']] * model_params['fp_depth']
conv_arch_params = {'num_hidden_features' : conv_layer_sizes,
'fp_length' : model_params['fp_length'], 'normalize' : 1}
loss_fun, pred_fun, conv_parser = \
build_conv_deep_net(conv_arch_params, vanilla_net_params, model_params['L2_reg'])
num_weights = len(conv_parser)
predict_func, trained_weights, conv_training_curve = \
train_nn(pred_fun, loss_fun, num_weights, train_inputs, train_targets,
train_params, validation_smiles=val_inputs, validation_raw_targets=val_targets)
test_predictions = predict_func(test_inputs)
return rmse(test_predictions, test_targets)
