def build_predictor(net_type, fp_length, fp_depth, conv_width, h1_size, L2_reg,
nll_func):
if net_type == 'mean':
return build_mean_predictor(nll_func)
elif net_type == 'conv_plus_linear':
vanilla_net_params = dict(layer_sizes=[fp_length],
normalize=True,
L2_reg=L2_reg,
nll_func=nll_func)
conv_params = dict(num_hidden_features=[conv_width] * fp_depth,
fp_length=fp_length)
return build_conv_deep_net(conv_params, vanilla_net_params)
elif net_type == 'morgan_plus_linear':
vanilla_net_params = dict(layer_sizes=[fp_length],
normalize=True,
L2_reg=L2_reg,
nll_func=nll_func)
return build_morgan_deep_net(fp_length, fp_depth, vanilla_net_params)
elif net_type == 'conv_plus_net':
vanilla_net_params = dict(layer_sizes=[fp_length, h1_size],
normalize=True,
L2_reg=L2_reg,
nll_func=nll_func)
conv_params = dict(num_hidden_features=[conv_width] * fp_depth,
fp_length=fp_length)
return build_conv_deep_net(conv_params, vanilla_net_params)
elif net_type == 'morgan_plus_net':
vanilla_net_params = dict(layer_sizes=[fp_length, h1_size],
normalize=True,
L2_reg=L2_reg,
nll_func=nll_func)
return build_morgan_deep_net(fp_length, fp_depth, vanilla_net_params)
else:
raise Exception("Unknown network type.")
def run_morgan_experiment():
loss_fun, pred_fun, net_parser = \
build_morgan_deep_net(model_params['fp_length'],
model_params['fp_depth'], vanilla_net_params)
num_weights = len(net_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)
return print_performance(predict_func)
def run_morgan_experiment():
loss_fun, pred_fun, net_parser = \
build_morgan_deep_net(model_params['fp_length'],
model_params['fp_depth'], vanilla_net_params)
num_weights = len(net_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)
return print_performance(predict_func)
def morg_fp_func():
loss, _, parser = build_morgan_deep_net(fp_length, 1, vanilla_net_params)
return lambda weights: loss(weights, smiles, targets), parser
def morg_fp_func():
loss, _, parser = build_morgan_deep_net(fp_length, 1, vanilla_net_params)
return lambda weights: loss(weights, smiles, targets), parser
