layer_5_de=SigmoidConvLayer(filter_size=(1,1),
num_filters=64,
num_channels=64,
fm_size=(1,1),
batch_size=batch_size,
border_mode="same")
# learning rate formula:
# r = 1 - 0.5*(ni/ntot)*(ni/ntot)
# ni = ith layer; ntot = number of layers
# layer_0
model_0=ConvAutoEncoder(layers=[layer_0_en, MaxPoolingSameSize(pool_size=(4,4)), layer_0_de])
out_0=model_0.fprop(images, corruption_level=corruption_level)
cost_0=mean_square_cost(out_0[-1], images)+L2_regularization(model_0.params, 0.005)
updates_0=gd_updates(cost=cost_0, params=model_0.params, method="sgd", learning_rate=0.1)
# layer_0 --> layer_1
model_0_to_1=FeedForward(layers=[layer_0_en, MaxPooling(pool_size=(2,2))]);
out_0_to_1=model_0_to_1.fprop(images);
# layer_1
model_1=ConvAutoEncoder(layers=[layer_1_en, MaxPoolingSameSize(pool_size=(2,2)), layer_1_de])
out_1=model_1.fprop(out_0_to_1[-1], corruption_level=corruption_level)
cost_1=mean_square_cost(out_1[-1], out_0_to_1[-1])+L2_regularization(model_1.params, 0.005)
updates_1=gd_updates(cost=cost_1, params=model_1.params, method="sgd", learning_rate=0.1)
# layer_1 --> layer_2
model_1_to_2=FeedForward(layers=[layer_1_en, MaxPooling(pool_size=(4,4))]);
out_1_to_2=model_1_to_2.fprop(images);
batch_size=batch_size,
border_mode="same")
layer_4_de = SigmoidConvLayer(filter_size=(1, 1),
num_filters=128,
num_channels=128,
fm_size=(1, 1),
batch_size=batch_size,
border_mode="same")
# layer_0
model_0 = ConvAutoEncoder(
layers=[layer_0_en,
MaxPoolingSameSize(pool_size=(4, 4)), layer_0_de])
out_0 = model_0.fprop(images, corruption_level=corruption_level)
cost_0 = mean_square_cost(out_0[-1], images) + L2_regularization(
model_0.params, 0.005)
updates_0 = gd_updates(cost=cost_0,
params=model_0.params,
method="sgd",
learning_rate=0.1)
# layer_0 --> layer_1
model_0_to_1 = FeedForward(
layers=[layer_0_en, MaxPooling(pool_size=(4, 4))])
out_0_to_1 = model_0_to_1.fprop(images)
# layer_1
model_1 = ConvAutoEncoder(
layers=[layer_1_en,
MaxPoolingSameSize(pool_size=(2, 2)), layer_1_de])
layer_3_en=ReLUConvLayer(filter_size=(3,3),
num_filters=50,
num_channels=50,
fm_size=(18,18),
batch_size=batch_size)
layer_3_de=SigmoidConvLayer(filter_size=(3,3),
num_filters=50,
num_channels=50,
fm_size=(16,16),
batch_size=batch_size,
border_mode="full")
model_0=ConvAutoEncoder(layers=[layer_0_en, layer_0_de])
out_0=model_0.fprop(images, corruption_level=corruption_level)
cost_0=mean_square_cost(out_0[-1], images)+L2_regularization(model_0.params, 0.005)
updates_0=gd_updates(cost=cost_0, params=model_0.params, method="sgd", learning_rate=0.1)
model_1=ConvAutoEncoder(layers=[layer_1_en, layer_1_de])
out_1=model_1.fprop(out_0[0], corruption_level=corruption_level)
cost_1=mean_square_cost(out_1[-1], out_0[0])+L2_regularization(model_1.params, 0.005)
updates_1=gd_updates(cost=cost_1, params=model_1.params, method="sgd", learning_rate=0.1)
model_2=ConvAutoEncoder(layers=[layer_2_en, layer_2_de])
out_2=model_2.fprop(out_1[0], corruption_level=corruption_level)
cost_2=mean_square_cost(out_2[-1], out_1[0])+L2_regularization(model_2.params, 0.005)
updates_2=gd_updates(cost=cost_2, params=model_2.params, method="sgd", learning_rate=0.1)
model_3=ConvAutoEncoder(layers=[layer_3_en, layer_3_de])
out_3=model_3.fprop(out_2[0], corruption_level=corruption_level)
cost_3=mean_square_cost(out_3[-1], out_2[0])+L2_regularization(model_3.params, 0.005)
# layer_3_en=ReLUConvLayer(filter_size=(3,3),
# num_filters=50,
# num_channels=50,
# fm_size=(18,18),
# batch_size=batch_size)
# layer_3_de=SigmoidConvLayer(filter_size=(3,3),
# num_filters=50,
# num_channels=50,
# fm_size=(16,16),
# batch_size=batch_size,
# border_mode="full")
model_0=ConvAutoEncoder(layers=[layer_0_en, layer_0_de])
out_0=model_0.fprop(images, corruption_level=corruption_level)
cost_0=mean_square_cost(out_0[-1], images)+L2_regularization(model_0.params, 0.005)
updates_0=gd_updates(cost=cost_0, params=model_0.params, method="sgd", learning_rate=0.1)
## append a max-pooling layer
model_trans=FeedForward(layers=[layer_0_en, MaxPooling(pool_size=(2,2))]);
out_trans=model_trans.fprop(images);
model_1=ConvAutoEncoder(layers=[layer_1_en, layer_1_de])
out_1=model_1.fprop(out_trans[-1], corruption_level=corruption_level)
cost_1=mean_square_cost(out_1[-1], out_trans[-1])+L2_regularization(model_1.params, 0.005)
updates_1=gd_updates(cost=cost_1, params=model_1.params, method="sgd", learning_rate=0.1)
# model_2=ConvAutoEncoder(layers=[layer_2_en, layer_2_de])
# out_2=model_2.fprop(out_1[0], corruption_level=corruption_level)
# layer_3_en=ReLUConvLayer(filter_size=(3,3),
# num_filters=50,
# num_channels=50,
# fm_size=(18,18),
# batch_size=batch_size)
# layer_3_de=SigmoidConvLayer(filter_size=(3,3),
# num_filters=50,
# num_channels=50,
# fm_size=(16,16),
# batch_size=batch_size,
# border_mode="full")
model_0 = ConvAutoEncoder(layers=[layer_0_en, layer_0_de])
out_0 = model_0.fprop(images, corruption_level=corruption_level)
cost_0 = mean_square_cost(out_0[-1], images) + L2_regularization(
model_0.params, 0.005)
updates_0 = gd_updates(cost=cost_0,
params=model_0.params,
method="sgd",
learning_rate=0.1)
## append a max-pooling layer
model_trans = FeedForward(
layers=[layer_0_en, MaxPooling(pool_size=(2, 2))])
out_trans = model_trans.fprop(images)
model_1 = ConvAutoEncoder(layers=[layer_1_en, layer_1_de])
out_1 = model_1.fprop(out_trans[-1], corruption_level=corruption_level)
cost_1 = mean_square_cost(out_1[-1], out_trans[-1]) + L2_regularization(
num_channels=50,
fm_size=(8,8),
batch_size=batch_size,
border_mode="same")
layer_1_de=SigmoidConvLayer(filter_size=(2,2),
num_filters=50,
num_channels=50,
fm_size=(8,8),
batch_size=batch_size,
border_mode="same")
model_0=ConvAutoEncoder(layers=[layer_0_en, MaxPoolingSameSize(pool_size=(4,4)), layer_0_de])
out_0=model_0.fprop(images, corruption_level=corruption_level)
cost_0=mean_square_cost(out_0[-1], images)+L2_regularization(model_0.params, 0.005)
updates_0=gd_updates(cost=cost_0, params=model_0.params, method="sgd", learning_rate=0.1)
## append a max-pooling layer
model_trans=FeedForward(layers=[layer_0_en, MaxPooling(pool_size=(4,4))]);
out_trans=model_trans.fprop(images);
model_1=ConvAutoEncoder(layers=[layer_1_en, MaxPoolingSameSize(pool_size=(2,2)), layer_1_de])
out_1=model_1.fprop(out_trans[-1], corruption_level=corruption_level)
cost_1=mean_square_cost(out_1[-1], out_trans[-1])+L2_regularization(model_1.params, 0.005)
updates_1=gd_updates(cost=cost_1, params=model_1.params, method="sgd", learning_rate=0.1)
train_0=theano.function(inputs=[idx, corruption_level],
layer_3_en = ReLUConvLayer(filter_size=(3, 3),
num_filters=50,
num_channels=50,
fm_size=(18, 18),
batch_size=batch_size)
layer_3_de = SigmoidConvLayer(filter_size=(3, 3),
num_filters=50,
num_channels=50,
fm_size=(16, 16),
batch_size=batch_size,
border_mode="full")
model_0 = ConvAutoEncoder(layers=[layer_0_en, layer_0_de])
out_0 = model_0.fprop(images, corruption_level=corruption_level)
cost_0 = mean_square_cost(out_0[-1], images) + L2_regularization(
model_0.params, 0.005)
updates_0 = gd_updates(cost=cost_0,
params=model_0.params,
method="sgd",
learning_rate=0.1)
model_1 = ConvAutoEncoder(layers=[layer_1_en, layer_1_de])
out_1 = model_1.fprop(out_0[0], corruption_level=corruption_level)
cost_1 = mean_square_cost(out_1[-1], out_0[0]) + L2_regularization(
model_1.params, 0.005)
updates_1 = gd_updates(cost=cost_1,
params=model_1.params,
method="sgd",
learning_rate=0.1)
