X = T.matrix("data")
y = T.ivector("label")
idx = T.lscalar()
ep_idx = T.lscalar()
corruption_level = T.fscalar()
encode_layer = SigmoidLayer(in_dim=784, out_dim=500)
decode_layer = SigmoidLayer(in_dim=500, out_dim=784)
model = AutoEncoder(layers=[encode_layer, decode_layer])
#out=model.fprop(X, corruption_level=corruption_level, noise_type="gaussian");
out = model.fprop(X, corruption_level=corruption_level)
cost = binary_cross_entropy_cost(out[-1], X)
updates = gd_updates(cost=cost,
params=model.params,
method="sgd",
learning_rate=0.1)
train = theano.function(
inputs=[idx, corruption_level],
outputs=[cost],
updates=updates,
givens={X: train_set_x[idx * batch_size:(idx + 1) * batch_size]})
print "[MESSAGE] The model is built"
epoch = 0
y=T.ivector("label");
idx=T.lscalar();
ep_idx=T.lscalar();
corruption_level=T.fscalar();
encode_layer=SigmoidLayer(in_dim=784,
out_dim=500);
decode_layer=SigmoidLayer(in_dim=500,
out_dim=784);
model=AutoEncoder(layers=[encode_layer, decode_layer]);
#out=model.fprop(X, corruption_level=corruption_level, noise_type="gaussian");
out=model.fprop(X, corruption_level=corruption_level);
cost=binary_cross_entropy_cost(out[-1], X);
updates=gd_updates(cost=cost, params=model.params, method="sgd", learning_rate=0.1);
train=theano.function(inputs=[idx, corruption_level],
outputs=[cost],
updates=updates,
givens={X: train_set_x[idx * batch_size: (idx + 1) * batch_size]});
print "[MESSAGE] The model is built"
epoch = 0;
min_cost=None;
corr=np.random.uniform(low=0.2, high=0.3, size=1).astype("float32");
corr_best=corr[0]
while (epoch < n_epochs):
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size
print "[MESSAGE] The data is loaded"
X = T.matrix("data")
idx = T.lscalar()
noise = theano.shared(np.asarray(np.random.normal(scale=0.1,
size=(batch_size, 784)),
dtype="float32"),
borrow=True)
#corrupted=corrupt_input(X, corruption_level=noise, noise_type="gaussian");
corrupted = X + noise
cost = binary_cross_entropy_cost(corrupted, X)
updates = gd_updates(cost, [noise], method="sgd", learning_rate=0.001)
train = theano.function(
inputs=[idx],
outputs=[cost],
updates=updates,
givens={X: train_set_x[idx * batch_size:(idx + 1) * batch_size]})
epoch = 0
while (epoch < n_epochs):
epoch = epoch + 1
c = []
for batch_index in xrange(n_train_batches):
n_train_batches=train_set_x.get_value(borrow=True).shape[0]/batch_size;
n_valid_batches=valid_set_x.get_value(borrow=True).shape[0]/batch_size;
n_test_batches=test_set_x.get_value(borrow=True).shape[0]/batch_size;
print "[MESSAGE] The data is loaded"
X=T.matrix("data");
idx=T.lscalar();
noise=theano.shared(np.asarray(np.random.normal(scale=0.1, size=(batch_size, 784)),
dtype="float32"),
borrow=True);
#corrupted=corrupt_input(X, corruption_level=noise, noise_type="gaussian");
corrupted=X+noise;
cost=binary_cross_entropy_cost(corrupted, X);
updates=gd_updates(cost, [noise], method="sgd", learning_rate=0.001);
train=theano.function(inputs=[idx],
outputs=[cost],
updates=updates,
givens={X: train_set_x[idx * batch_size: (idx + 1) * batch_size]});
epoch = 0;
while (epoch < n_epochs):
epoch = epoch + 1;
c = [];
for batch_index in xrange(n_train_batches):
train_cost=train(batch_index);