print ' ', np.mean(c_4), str(corr_best[4][0]), min_cost[4], max_iter[4] print ' ', np.mean(c_5), str(corr_best[5][0]), min_cost[5], max_iter[5] print "[MESSAGE] The model is trained" ################################## BUILD SUPERVISED MODEL ####################################### pool_0=MaxPooling(pool_size=(2,2)); pool_1=MaxPooling(pool_size=(4,4)); 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],
print ' ', np.mean(c_3), str( corr_best[3][0]), min_cost[3], max_iter[3] print ' ', np.mean(c_4), str( corr_best[4][0]), min_cost[4], max_iter[4] print "[MESSAGE] The model is trained" ################################## BUILD SUPERVISED MODEL ####################################### 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(
print ' ', np.mean(c_3), str( corr_best[3][0]), min_cost[3], max_iter[3] print ' ', np.mean(c_4), str( corr_best[4][0]), min_cost[4], max_iter[4] print "[MESSAGE] The model is trained" ################################## BUILD SUPERVISED MODEL ####################################### 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=50 * 1 * 1, out_dim=25) layer_6 = SoftmaxLayer(in_dim=25, 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(
print 'Training epoch %d, cost ' % epoch, np.mean(c_0), str( corr_best[0][0]), min_cost[0], max_iter[0] print ' ', np.mean(c_1), str( 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 ####################################### pool_0 = MaxPooling(pool_size=(2, 2)) pool_1 = MaxPooling(pool_size=(2, 2)) flattener = Flattener() layer_2 = ReLULayer(in_dim=50 * 5 * 5, out_dim=500) layer_3 = SoftmaxLayer(in_dim=500, out_dim=10) # model_sup=FeedForward(layers=[layer_0_en, layer_1_en, flattener, layer_5, layer_6]) model_sup = FeedForward(layers=[ layer_0_en, pool_0, layer_1_en, pool_1, flattener, layer_2, layer_3 ]) 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(
border_mode="full") pool_0 = MaxPooling(pool_size=(2, 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(
max_iter[1]=0 else: max_iter[1]+=1 print 'Training epoch %d, cost ' % epoch, np.mean(c_0), str(corr_best[0][0]), min_cost[0], max_iter[0] print ' ', np.mean(c_1), str(corr_best[1][0]), min_cost[1], max_iter[1] print "[MESSAGE] The model is trained" ################################## BUILD SUPERVISED MODEL ####################################### pool_0=MaxPooling(pool_size=(4,4)); pool_1=MaxPooling(pool_size=(2,2)); flattener=Flattener() layer_2=ReLULayer(in_dim=50*4*4, out_dim=400) layer_3=SoftmaxLayer(in_dim=400, out_dim=10) model_sup=FeedForward(layers=[layer_0_en, pool_0, layer_1_en, pool_1, flattener, layer_2, layer_3]) 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]})
print 'Training epoch %d, cost ' % epoch, np.mean(c_0), str( corr_best[0][0]), min_cost[0], max_iter[0] print ' ', np.mean(c_1), str( 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],