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])
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
layer_1 = LCNLayer(filter_size=(5, 5),
num_filters=32,
num_channels=64,
fm_size=(16, 16),
batch_size=batch_size,
border_mode="full")
pool_1 = MaxPooling(pool_size=(2, 2))
flattener = Flattener()
layer_2 = ReLULayer(in_dim=32 * 64, out_dim=800)
layer_3 = SoftmaxLayer(in_dim=800, out_dim=10)
model = FeedForward(
layers=[layer_0, pool_0, layer_1, pool_1, flattener, layer_2, layer_3])
out = model.fprop(images)
cost = categorical_cross_entropy_cost(out[-1], y)
updates = gd_updates(cost=cost,
params=model.params,
method="sgd",
learning_rate=0.01,
momentum=0.9)
extract = theano.function(
inputs=[idx],
outputs=layer_0.apply(images),
givens={X: train_set_x[idx * batch_size:(idx + 1) * batch_size]})
print extract(1).shape
# 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)
# 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)
corr_best[1][0]), min_cost[1], max_iter[1]
print ' ', np.mean(c_2), str(
corr_best[2][0]), min_cost[2], max_iter[2]
print ' ', np.mean(c_3), str(
corr_best[3][0]), min_cost[3], max_iter[3]
print "[MESSAGE] The model is trained"
################################## BUILD SUPERVISED MODEL #######################################
flattener = Flattener()
layer_5 = ReLULayer(in_dim=50 * 16 * 16, out_dim=1000)
layer_6 = SoftmaxLayer(in_dim=1000, out_dim=10)
model_sup = FeedForward(layers=[
layer_0_en, layer_1_en, layer_2_en, layer_3_en, flattener, layer_5, layer_6
])
out_sup = model_sup.fprop(images)
cost_sup = categorical_cross_entropy_cost(out_sup[-1], y)
updates = gd_updates(cost=cost_sup,
params=model_sup.params,
method="sgd",
learning_rate=0.1)
train_sup = theano.function(
inputs=[idx],
outputs=cost_sup,
updates=updates,
givens={
X: train_set_x[idx * batch_size:(idx + 1) * batch_size],
