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")
y = T.ivector("label")
idx = T.lscalar()
dropout_rate = T.fscalar()
layer_0 = ReLULayer(in_dim=784, out_dim=500)
layer_1 = ReLULayer(in_dim=500, out_dim=200)
layer_2 = SoftmaxLayer(in_dim=200, out_dim=10)
dropout = multi_dropout([(batch_size, 784), (batch_size, 500), (batch_size, 200)], dropout_rate)
model = FeedForward(layers=[layer_0, layer_1, layer_2], dropout=dropout)
model_test = FeedForward(layers=[layer_0, layer_1, layer_2])
# model=FeedForward(layers=[layer_0, layer_1, layer_2]);
out = model.fprop(X)
out_test = model_test.fprop(X)
cost = categorical_cross_entropy_cost(out[-1], y)
# Configurations that work
updates = gd_updates(cost=cost, params=model.params, method="sgd", learning_rate=0.01, momentum=0.9)
train = theano.function(
inputs=[idx, dropout_rate],
outputs=cost,
n_train_batches = train_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")
y = T.ivector("label")
idx = T.lscalar()
dropout_rate = T.fscalar()
layer_0 = ReLULayer(in_dim=1024, out_dim=500)
layer_1 = ReLULayer(in_dim=500, out_dim=200)
layer_2 = SoftmaxLayer(in_dim=200, out_dim=10)
dropout = multi_dropout([(batch_size, 1024), (batch_size, 500),
(batch_size, 200)], dropout_rate)
model = FeedForward(layers=[layer_0, layer_1, layer_2], dropout=dropout)
model_test = FeedForward(layers=[layer_0, layer_1, layer_2])
#model=FeedForward(layers=[layer_0, layer_1, layer_2]);
out = model.fprop(X)
out_test = model_test.fprop(X)
cost = categorical_cross_entropy_cost(out[-1], y)
# Configurations that work
updates = gd_updates(cost=cost,
params=model.params,
method="sgd",
learning_rate=0.01,
momentum=0.9)
num_filters=20,
num_channels=50,
fm_size=(11, 11),
batch_size=batch_size)
pool_1 = MaxPooling(pool_size=(2, 2))
flattener = Flattener()
layer_2 = ReLULayer(in_dim=320, out_dim=200)
layer_3 = SoftmaxLayer(in_dim=200, out_dim=10)
#dropout=multi_dropout([(batch_size, 1, 28, 28), None, (batch_size, 50, 11, 11), None, None, None, None], prob=0.5);
dropout = multi_dropout([(batch_size, 1, 28, 28), None,
(batch_size, 50, 11, 11), None, None, None, None],
prob=0.5)
model = FeedForward(
layers=[layer_0, pool_0, layer_1, pool_1, flattener, layer_2, layer_3],
dropout=dropout)
out = model.fprop(images)
cost = categorical_cross_entropy_cost(out[-1], y) + L2_regularization(
model.params, 0.01)
updates = gd_updates(cost=cost,
params=model.params,
method="sgd",
learning_rate=0.1)
train = theano.function(
num_channels=50,
fm_size=(11,11),
batch_size=batch_size);
pool_1=MaxPooling(pool_size=(2,2));
flattener=Flattener();
layer_2=ReLULayer(in_dim=320,
out_dim=200);
layer_3=SoftmaxLayer(in_dim=200,
out_dim=10);
#dropout=multi_dropout([(batch_size, 1, 28, 28), None, (batch_size, 50, 11, 11), None, None, None, None], prob=0.5);
dropout=multi_dropout([(batch_size, 1, 28, 28), None, (batch_size, 50, 11, 11), None, None, None, None], prob=0.5);
model=FeedForward(layers=[layer_0, pool_0, layer_1, pool_1, flattener, layer_2, layer_3],
dropout=dropout);
out=model.fprop(images);
cost=categorical_cross_entropy_cost(out[-1], y)+L2_regularization(model.params, 0.01);
updates=gd_updates(cost=cost, params=model.params, method="sgd", learning_rate=0.1);
train=theano.function(inputs=[idx],
outputs=cost,
updates=updates,
givens={X: train_set_x[idx * batch_size: (idx + 1) * batch_size],
y: train_set_y[idx * batch_size: (idx + 1) * batch_size]});
test=theano.function(inputs=[idx],
