log_reg = LogisticRegression(reg_input,lstm_2_hidden, 41) #lin_reg = LinearRegression(reg_input,lstm_2_hidden,1,True) log_reg.reconstruct(log_reg.p_y_given_x) #lin_reg.reconstruct(lin_reg.E_y_given_x) #reconstructed_regressions = T.concatenate([log_reg.reconstructed_x,lin_reg.reconstructed_x],axis=1) # #reverse_layer = LinearRegression(reconstructed_regressions, 2*lstm_2_hidden, lstm_2_hidden,False) lstm_3 = LSTM(rng,log_reg.reconstructed_x,lstm_2_hidden,lstm_1_hidden) lstm_4 = LSTM(rng,lstm_3.output,lstm_1_hidden,30) init_reg.reconstruct(lstm_4.output) difference = (ahead-init_reg.reconstructed_x) ** 2 encoder_cost = T.mean( difference ) cross_entropy_cost = T.mean(log_reg.cross_entropy_binary(y)) #y_hat_mean = T.mean(log_reg.p_y_given_x,axis=0) # #z_hat_mean = T.mean(lin_reg.E_y_given_x,axis=0) # #z_variance = lin_reg.E_y_given_x - z_hat_mean #z_var = z_variance.reshape((60,2,1)) #must reshape for outer product # #y_variance = log_reg.p_y_given_x - y_hat_mean
log_reg = LogisticRegression(reg_input, lstm_2_hidden, 41) #lin_reg = LinearRegression(reg_input,lstm_2_hidden,1,True) log_reg.reconstruct(log_reg.p_y_given_x) #lin_reg.reconstruct(lin_reg.E_y_given_x) #reconstructed_regressions = T.concatenate([log_reg.reconstructed_x,lin_reg.reconstructed_x],axis=1) # #reverse_layer = LinearRegression(reconstructed_regressions, 2*lstm_2_hidden, lstm_2_hidden,False) lstm_3 = LSTM(rng, log_reg.reconstructed_x, lstm_2_hidden, lstm_1_hidden) lstm_4 = LSTM(rng, lstm_3.output, lstm_1_hidden, 30) init_reg.reconstruct(lstm_4.output) difference = (ahead - init_reg.reconstructed_x)**2 encoder_cost = T.mean(difference) cross_entropy_cost = T.mean(log_reg.cross_entropy_binary(y)) #y_hat_mean = T.mean(log_reg.p_y_given_x,axis=0) # #z_hat_mean = T.mean(lin_reg.E_y_given_x,axis=0) # #z_variance = lin_reg.E_y_given_x - z_hat_mean #z_var = z_variance.reshape((60,2,1)) #must reshape for outer product # #y_variance = log_reg.p_y_given_x - y_hat_mean
