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()
params += inp_to_h3.get_params()
params += att_to_h1.get_params()
params += att_to_h2.get_params()
params += att_to_h3.get_params()
params += h1_to_h2.get_params()
params += h1_to_h3.get_params()
params += h2_to_h3.get_params()
biases += inp_to_h1.get_biases()
biases += inp_to_h2.get_biases()
biases += inp_to_h3.get_biases()
biases += att_to_h1.get_biases()
biases += att_to_h2.get_biases()
biases += att_to_h3.get_biases()
init_w = tensor.matrix("init_w")
init_w.tag.test_value = np_zeros((minibatch_size, n_chars))
params = []
w_conv1, = make_conv_weights(1, (n_kernels,), (conv_size1, input_dim),
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()
init_w = tensor.matrix("init_w")
init_w.tag.test_value = np_zeros((minibatch_size, n_chars))
params = []
w_conv1, = make_conv_weights(1, (n_kernels, ), (conv_size1, input_dim),
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()
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()
params += inp_to_h3.get_params()
params += att_to_h1.get_params()
params += att_to_h2.get_params()
params += att_to_h3.get_params()
params += h1_to_h2.get_params()
params += h1_to_h3.get_params()
params += h2_to_h3.get_params()
biases += inp_to_h1.get_biases()
biases += inp_to_h2.get_biases()
biases += inp_to_h3.get_biases()
biases += att_to_h1.get_biases()
biases += att_to_h2.get_biases()
biases += att_to_h3.get_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)
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()
params += inp_to_h3.get_params()
params += h1_to_h2.get_params()
params += h1_to_h3.get_params()
params += h2_to_h3.get_params()
biases += inp_to_h1.get_biases()
biases += inp_to_h2.get_biases()
biases += inp_to_h3.get_biases()
biases += h1_to_h2.get_biases()
biases += h1_to_h3.get_biases()
biases += h2_to_h3.get_biases()
h1_to_outs, = make_weights(n_hid, [n_proj], random_state)
h2_to_outs, = make_weights(n_hid, [n_proj], random_state)