init_h3.tag.test_value = np_zeros((minibatch_size, n_hid)) init_kappa = tensor.matrix("init_kappa") init_kappa.tag.test_value = np_zeros((minibatch_size, att_size)) init_w = tensor.matrix("init_w") init_w.tag.test_value = np_zeros((minibatch_size, n_chars)) params = [] biases = [] cell1 = GRU(input_dim, n_hid, random_state) cell2 = GRU(n_hid, n_hid, random_state) cell3 = GRU(n_hid, n_hid, random_state) params += cell1.get_params() params += cell2.get_params() params += cell3.get_params() inp_to_h1 = GRUFork(input_dim, n_hid, random_state) inp_to_h2 = GRUFork(input_dim, n_hid, random_state) inp_to_h3 = GRUFork(input_dim, n_hid, random_state) att_to_h1 = GRUFork(n_chars, n_hid, random_state) att_to_h2 = GRUFork(n_chars, n_hid, random_state) att_to_h3 = GRUFork(n_chars, n_hid, random_state) h1_to_h2 = GRUFork(n_hid, n_hid, random_state) h1_to_h3 = GRUFork(n_hid, n_hid, random_state) h2_to_h3 = GRUFork(n_hid, n_hid, random_state) params += inp_to_h1.get_params() params += inp_to_h2.get_params()
init_h3.tag.test_value = np_zeros((minibatch_size, n_hid)) init_kappa = tensor.matrix("init_kappa") init_kappa.tag.test_value = np_zeros((minibatch_size, att_size)) init_w = tensor.matrix("init_w") init_w.tag.test_value = np_zeros((minibatch_size, n_chars)) params = [] biases = [] cell1 = GRU(input_dim, n_hid, random_state) cell2 = GRU(n_hid, n_hid, random_state) cell3 = GRU(n_hid, n_hid, random_state) params += cell1.get_params() params += cell2.get_params() params += cell3.get_params() inp_to_h1 = GRUFork(input_dim, n_hid, random_state) inp_to_h2 = GRUFork(input_dim, n_hid, random_state) inp_to_h3 = GRUFork(input_dim, n_hid, random_state) att_to_h1 = GRUFork(n_chars, n_hid, random_state) att_to_h2 = GRUFork(n_chars, n_hid, random_state) att_to_h3 = GRUFork(n_chars, n_hid, random_state) h1_to_h2 = GRUFork(n_hid, n_hid, random_state) h1_to_h3 = GRUFork(n_hid, n_hid, random_state) h2_to_h3 = GRUFork(n_hid, n_hid, random_state) params += inp_to_h1.get_params() params += inp_to_h2.get_params()
random_state) b_conv1, = make_biases((n_kernels,)) w_conv2, = make_conv_weights(n_kernels, (n_kernels,), (conv_size2, 1), random_state) b_conv2, = make_biases((n_kernels,)) params += [w_conv1, b_conv1, w_conv2, b_conv2] # Use GRU classes only to fork 1 inp to 2 inp:gate pairs conv_to_h1 = GRUFork(n_kernels, n_hid, random_state) conv_to_h2 = GRUFork(n_kernels, n_hid, random_state) params += conv_to_h1.get_params() params += conv_to_h2.get_params() cell1 = GRU(n_kernels, n_hid, random_state) cell2 = GRU(n_hid, n_hid, random_state) params += cell1.get_params() params += cell2.get_params() # Use GRU classes only to fork 1 inp to 2 inp:gate pairs att_to_h1 = GRUFork(n_chars, n_hid, random_state) att_to_h2 = GRUFork(n_chars, n_hid, random_state) h1_to_h2 = GRUFork(n_hid, n_hid, random_state) params += att_to_h1.get_params() params += att_to_h2.get_params() params += h1_to_h2.get_params() h1_to_att_a, h1_to_att_b, h1_to_att_k = make_weights(n_hid, 3 * [att_size], random_state) params += [h1_to_att_a, h1_to_att_b, h1_to_att_k]
random_state) b_conv1, = make_biases((n_kernels, )) w_conv2, = make_conv_weights(n_kernels, (n_kernels, ), (conv_size2, 1), random_state) b_conv2, = make_biases((n_kernels, )) params += [w_conv1, b_conv1, w_conv2, b_conv2] # Use GRU classes only to fork 1 inp to 2 inp:gate pairs conv_to_h1 = GRUFork(n_kernels, n_hid, random_state) conv_to_h2 = GRUFork(n_kernels, n_hid, random_state) params += conv_to_h1.get_params() params += conv_to_h2.get_params() cell1 = GRU(n_kernels, n_hid, random_state) cell2 = GRU(n_hid, n_hid, random_state) params += cell1.get_params() params += cell2.get_params() # Use GRU classes only to fork 1 inp to 2 inp:gate pairs att_to_h1 = GRUFork(n_chars, n_hid, random_state) att_to_h2 = GRUFork(n_chars, n_hid, random_state) h1_to_h2 = GRUFork(n_hid, n_hid, random_state) params += att_to_h1.get_params() params += att_to_h2.get_params() params += h1_to_h2.get_params() h1_to_att_a, h1_to_att_b, h1_to_att_k = make_weights( n_hid, 3 * [att_size], random_state) params += [h1_to_att_a, h1_to_att_b, h1_to_att_k]