pool_0 = MaxPooling(pool_size=(4, 4)) pool_1 = MaxPooling(pool_size=(2, 2)) pool_2 = MaxPooling(pool_size=(2, 2)) pool_3 = MaxPooling(pool_size=(2, 2)) flattener = Flattener() layer_5 = ReLULayer(in_dim=128 * 1 * 1, out_dim=64) layer_6 = SoftmaxLayer(in_dim=64, out_dim=10) model_sup = FeedForward(layers=[ layer_0_en, pool_0, layer_1_en, pool_1, layer_2_en, pool_2, layer_3_en, pool_3, layer_4_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], y: train_set_y[idx * batch_size:(idx + 1) * batch_size] }) test_sup = theano.function(
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 train = theano.function( inputs=[idx], outputs=cost,
pool_2=MaxPooling(pool_size=(2,2)); pool_3=MaxPooling(pool_size=(2,2)); pool_4=MaxPooling(pool_size=(2,2)); flattener=Flattener() layer_6=ReLULayer(in_dim=64*1*1, out_dim=32) layer_7=SoftmaxLayer(in_dim=32, out_dim=6) model_sup=FeedForward(layers=[layer_0_en, pool_0, layer_1_en, pool_1, layer_2_en, pool_2, layer_3_en, pool_3, layer_4_en, pool_4, layer_5_en, flattener, layer_6, layer_7]) 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], y: train_set_y[idx * batch_size: (idx + 1) * batch_size]}) test_sup=theano.function(inputs=[idx], outputs=model_sup.layers[-1].error(out_sup[-1], y), givens={X: test_set_x[idx * batch_size: (idx + 1) * batch_size], y: test_set_y[idx * batch_size: (idx + 1) * batch_size]}) print "[MESSAGE] The supervised model is built"
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 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],