# pca = pickle.load(f)
# trX=pca.transform(trX)
# vlX=pca.transform(vlX)
# teX=pca.transform(teX)
# -----------------------SET PARAMETERS-------------------------#
losses_ratio = run_parameters.losses_ratio
supervised_cost_fun = run_parameters.supervised_cost_fun
# -----------------------CREATE RUN FUNCTIONS------------------#
# Creating the computation graph
print('Building computation graph')
input_var = T.fmatrix('input_var')
target_var = T.fmatrix('target_var')
labeled_var = T.fmatrix('labeled_var')
unsupervised_graph, supervised_graph, features = build_computation_graph(input_var, run_parameters)
# Train graph has dropout
reconstruction, prediction = layers.get_output([unsupervised_graph, supervised_graph])
# Test graph has no dropout so deterministic = True
test_reconstruction, test_prediction = layers.get_output([unsupervised_graph, supervised_graph], deterministic=True)
if run_parameters.clip_unsupervised_output is not None:
reconstruction = T.clip(reconstruction, run_parameters.clip_unsupervised_output[0],
run_parameters.clip_unsupervised_output[1])
test_reconstruction = T.clip(test_reconstruction, run_parameters.clip_unsupervised_output[0],
run_parameters.clip_unsupervised_output[1])
# Get all trainable params
params = layers.get_all_params(unsupervised_graph, trainable=True) + \
layers.get_all_params(supervised_graph, trainable=True)
# params = layers.get_all_params(supervised_graph)[-2:]
params = utils.unique(params)
#-----------------------SET PARAMETERS-------------------------#
# Set the dimension here, 1 list = 1 stack, 2 list = 2 stacks, etc...
# dimensions = [[1500, 3, 200]] # example of 1 stack
dimensions = [[1500, 3, 500], [1000, 3, 200]] # example of 3 stacks
# Set learning ratio for unsupervised, supervised and weights regularization
lr = (1.0, 1.0, 0)
# -----------------------CREATE RUN FUNCTIONS------------------#
# Creating the computation graph
print('Building computation graph')
input_shape = [None, IM_SIZE]
input_var = T.fmatrix('input_var')
target_var = T.fmatrix('target_var')
labeled_var = T.fmatrix('labeled_var')
unsupervised_graph, supervised_graph, features = build_computation_graph(input_var, input_shape, dimensions)
# Train graph has dropout
reconstruction = layers.get_output(unsupervised_graph)
prediction = layers.get_output(supervised_graph)
# Test graph has no dropout so deterministic = True
test_reconstruction = layers.get_output(unsupervised_graph, deterministic=True)
test_prediction = layers.get_output(supervised_graph, deterministic=True)
# Get all trainable params
params = layers.get_all_params(unsupervised_graph, trainable=True) + \
layers.get_all_params(supervised_graph, trainable=True)
params = utils.unique(params)
# Get regularizable params
regularization_params = layers.get_all_params(unsupervised_graph, regularizable=True) + \
layers.get_all_params(supervised_graph, regularizable=True)
