def train_dbn(path, epochs, learning_rate, dims):
#train dbn on mnist
data = mnist.load_mnist('../../../mnist_test.csv')
data = rbm.insert_biases(data)
dims = [784, 100, 50, 10]
dbn_weights = generate_dbn_weights(dims)
dbn_weights = train_dbm(dbm_weights, data, learning_rate = learning_rate, max_epochs = 300)
return vis_states
def reconstruct(weights, hid_states):
for i in range(len(weights) + 1)[1:]:
hid_states = rbm.reconstruct(weights[-i], hid_states)
return hid_states
def pickle_weights(weights, path):
data = {"weights": weights}
output = open(path, "wb")
pickle.dump(data, output)
output.close()
def unpickle(path):
pkl_file = open(path, "rb")
data = pickle.load(pkl_file)
pkl_file.close()
return data["weights"]
if __name__ == "__main__":
data = mnist.load_mnist("../../../mnist_test.csv")
data = rbm.insert_biases(data)
dims = [784, 200, 50, 10]
dbn_weights = generate_dbn_weights(dims)
dbn_weights = unpickle("../weights/300_dbn.pkl")
# dbn_weights = train_dbn(dbn_weights, data, max_epochs = 2000)
return dbn_weights
def construct(weights, vis_states):
for weight in weights:
vis_states = rbm.construct(weight, vis_states)
return vis_states
def reconstruct(weights, hid_states):
for i in range(len(weights)+1)[1:]:
hid_states = rbm.reconstruct(weights[-i], hid_states)
return hid_states
def pickle_weights(weights, path):
data = {'weights': weights}
output = open(path, 'wb')
pickle.dump(data, output)
output.close()
def unpickle(path):
pkl_file = open(path, 'rb')
data = pickle.load(pkl_file)
pkl_file.close()
return data['weights']
if __name__ == "__main__":
data = mnist.load_mnist('../../../mnist_test.csv')
data = rbm.insert_biases(data)
dims = [784, 200, 50, 10]
dbn_weights = generate_dbn_weights(dims)
dbn_weights = unpickle('../weights/300_dbn.pkl')
#dbn_weights = train_dbn(dbn_weights, data, max_epochs = 2000)
