# # Copyright (C) 2019 The Android Open Source Project # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Bidirectional Sequence LSTM Test: # FLOAT32, No Layer Normalization, No Cifg, No Peephole, No Projection, and No Clipping. def test(name, n_batch, n_fw_input, n_bw_input, n_cell, n_output, max_time, input_data=[], fw_input_to_input_weights_data=[], fw_input_to_forget_weights_data=[], fw_input_to_cell_weights_data=[], fw_input_to_output_weights_data=[], fw_recurrent_to_input_weights_data=[], fw_recurrent_to_forget_weights_data=[], fw_recurrent_to_cell_weights_data=[], fw_recurrent_to_output_weights_data=[], fw_cell_to_input_weights_data=[], fw_cell_to_forget_weights_data=[], fw_cell_to_output_weights_data=[], fw_input_gate_bias_data=[], fw_forget_gate_bias_data=[], fw_cell_bias_data=[], fw_output_gate_bias_data=[], fw_projection_weights_data=[], fw_projection_bias_data=[], bw_input_to_input_weights_data=[], bw_input_to_forget_weights_data=[], bw_input_to_cell_weights_data=[], bw_input_to_output_weights_data=[], bw_recurrent_to_input_weights_data=[], bw_recurrent_to_forget_weights_data=[], bw_recurrent_to_cell_weights_data=[], bw_recurrent_to_output_weights_data=[], bw_cell_to_input_weights_data=[], bw_cell_to_forget_weights_data=[], bw_cell_to_output_weights_data=[], bw_input_gate_bias_data=[], bw_forget_gate_bias_data=[], bw_cell_bias_data=[], bw_output_gate_bias_data=[], bw_projection_weights_data=[], bw_projection_bias_data=[], fw_activation_state_data=[], fw_cell_state_data=[], bw_activation_state_data=[], bw_cell_state_data=[], aux_input_data=[], fw_aux_input_to_input_weights_data=[], fw_aux_input_to_forget_weights_data=[], fw_aux_input_to_cell_weights_data=[], fw_aux_input_to_output_weights_data=[], bw_aux_input_to_input_weights_data=[], bw_aux_input_to_forget_weights_data=[], bw_aux_input_to_cell_weights_data=[], bw_aux_input_to_output_weights_data=[], fw_input_layer_norm_weights_data=[], fw_forget_layer_norm_weights_data=[], fw_cell_layer_norm_weights_data=[], fw_output_layer_norm_weights_data=[], bw_input_layer_norm_weights_data=[], bw_forget_layer_norm_weights_data=[], bw_cell_layer_norm_weights_data=[], bw_output_layer_norm_weights_data=[], fw_output_data=[], bw_output_data=[], ignore_fw_output=False): input = Input("input", "TENSOR_FLOAT32", "{{{}, {}, {}}}".format(max_time, n_batch, n_fw_input)) fw_input_to_input_weights = Input("fw_input_to_input_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_fw_input)) fw_input_to_forget_weights = Input("fw_input_to_forget_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_fw_input)) fw_input_to_cell_weights = Input("fw_input_to_cell_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_fw_input)) fw_input_to_output_weights = Input("fw_input_to_output_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_fw_input)) fw_recurrent_to_input_weights = Input("fw_recurrent_to_input_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_output)) fw_recurrent_to_forget_weights = Input("fw_recurrent_to_forget_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_output)) fw_recurrent_to_cell_weights = Input("fw_recurrent_to_cell_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_output)) fw_recurrent_to_output_weights = Input("fw_recurrent_to_output_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_output)) fw_cell_to_input_weights = Input("fw_cell_to_input_weights", "TENSOR_FLOAT32", "{0}") fw_cell_to_forget_weights = Input("fw_cell_to_forget_weights", "TENSOR_FLOAT32", "{0}") fw_cell_to_output_weights = Input("fw_cell_to_output_weights", "TENSOR_FLOAT32", "{0}") fw_input_gate_bias = Input("fw_input_gate_bias", "TENSOR_FLOAT32", "{{{}}}".format(n_cell)) fw_forget_gate_bias = Input("fw_forget_gate_bias", "TENSOR_FLOAT32", "{{{}}}".format(n_cell)) fw_cell_bias = Input("fw_cell_bias", "TENSOR_FLOAT32", "{{{}}}".format(n_cell)) fw_output_gate_bias = Input("fw_output_gate_bias", "TENSOR_FLOAT32", "{{{}}}".format(n_cell)) fw_projection_weights = Input("fw_projection_weights", "TENSOR_FLOAT32", "{0}") fw_projection_bias = Input("fw_projection_bias", "TENSOR_FLOAT32", "{0}") bw_input_to_input_weights = Input("bw_input_to_input_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_bw_input)) bw_input_to_forget_weights = Input("bw_input_to_forget_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_bw_input)) bw_input_to_cell_weights = Input("bw_input_to_cell_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_bw_input)) bw_input_to_output_weights = Input("bw_input_to_output_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_bw_input)) bw_recurrent_to_input_weights = Input("bw_recurrent_to_input_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_output)) bw_recurrent_to_forget_weights = Input("bw_recurrent_to_forget_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_output)) bw_recurrent_to_cell_weights = Input("bw_recurrent_to_cell_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_output)) bw_recurrent_to_output_weights = Input("bw_recurrent_to_output_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_output)) bw_cell_to_input_weights = Input("bw_cell_to_input_weights", "TENSOR_FLOAT32", "{0}") bw_cell_to_forget_weights = Input("bw_cell_to_forget_weights", "TENSOR_FLOAT32", "{0}") bw_cell_to_output_weights = Input("bw_cell_to_output_weights", "TENSOR_FLOAT32", "{0}") bw_input_gate_bias = Input("bw_input_gate_bias", "TENSOR_FLOAT32", "{{{}}}".format(n_cell)) bw_forget_gate_bias = Input("bw_forget_gate_bias", "TENSOR_FLOAT32", "{{{}}}".format(n_cell)) bw_cell_bias = Input("bw_cell_bias", "TENSOR_FLOAT32", "{{{}}}".format(n_cell)) bw_output_gate_bias = Input("bw_output_gate_bias", "TENSOR_FLOAT32", "{{{}}}".format(n_cell)) bw_projection_weights = Input("bw_projection_weights", "TENSOR_FLOAT32", "{0}") bw_projection_bias = Input("bw_projection_bias", "TENSOR_FLOAT32", "{0}") fw_activation_state = Input("fw_activatiom_state", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_batch, n_output)) fw_cell_state = Input("fw_cell_state", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_batch, n_cell)) bw_activation_state = Input("bw_activatiom_state", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_batch, n_output)) bw_cell_state = Input("bw_cell_state", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_batch, n_cell)) aux_input = Input("input", "TENSOR_FLOAT32", "{{{}, {}, {}}}".format(max_time, n_batch, n_bw_input)) fw_aux_input_to_input_weights = Input("fw_aux_input_to_input_weights", "TENSOR_FLOAT32", "{0}") fw_aux_input_to_forget_weights = Input("fw_input_to_forget_weights", "TENSOR_FLOAT32", "{0}") fw_aux_input_to_cell_weights = Input("fw_aux_input_to_cell_weights", "TENSOR_FLOAT32", "{0}") fw_aux_input_to_output_weights = Input("fw_aux_input_to_output_weights", "TENSOR_FLOAT32", "{0}") bw_aux_input_to_input_weights = Input("bw_aux_input_to_input_weights", "TENSOR_FLOAT32", "{0}") bw_aux_input_to_forget_weights = Input("bw_input_to_forget_weights", "TENSOR_FLOAT32", "{0}") bw_aux_input_to_cell_weights = Input("bw_aux_input_to_cell_weights", "TENSOR_FLOAT32", "{0}") bw_aux_input_to_output_weights = Input("bw_aux_input_to_output_weights", "TENSOR_FLOAT32", "{0}") fw_input_layer_norm_weights = Input("input_layer_norm_weights", "TENSOR_FLOAT32", "{0}") fw_forget_layer_norm_weights = Input("forget_layer_norm_weights", "TENSOR_FLOAT32", "{0}") fw_cell_layer_norm_weights = Input("cell_layer_norm_weights", "TENSOR_FLOAT32", "{0}") fw_output_layer_norm_weights = Input("output_layer_norm_weights", "TENSOR_FLOAT32", "{0}") bw_input_layer_norm_weights = Input("input_layer_norm_weights", "TENSOR_FLOAT32", "{0}") bw_forget_layer_norm_weights = Input("forget_layer_norm_weights", "TENSOR_FLOAT32", "{0}") bw_cell_layer_norm_weights = Input("cell_layer_norm_weights", "TENSOR_FLOAT32", "{0}") bw_output_layer_norm_weights = Input("output_layer_norm_weights", "TENSOR_FLOAT32", "{0}") fw_output = Output("fw_output", "TENSOR_FLOAT32", "{{{}, {}, {}}}".format(max_time, n_batch, n_output)) bw_output = Output("bw_output", "TENSOR_FLOAT32", "{{{}, {}, {}}}".format(max_time, n_batch, n_output)) if ignore_fw_output: fw_output = IgnoredOutput( "fw_output", "TENSOR_FLOAT32", "{{{}, {}, {}}}".format(max_time, n_batch, n_output)) activation = Int32Scalar("activation", 4) cell_clip = Float32Scalar("cell_clip", 0.0) proj_clip = Float32Scalar("proj_clip", 0.0) merge_outputs = BoolScalar("merge_outputs", False) time_major = BoolScalar("time_major", True) model = Model().Operation( "BIDIRECTIONAL_SEQUENCE_LSTM", input, fw_input_to_input_weights, fw_input_to_forget_weights, fw_input_to_cell_weights, fw_input_to_output_weights, fw_recurrent_to_input_weights, fw_recurrent_to_forget_weights, fw_recurrent_to_cell_weights, fw_recurrent_to_output_weights, fw_cell_to_input_weights, fw_cell_to_forget_weights, fw_cell_to_output_weights, fw_input_gate_bias, fw_forget_gate_bias, fw_cell_bias, fw_output_gate_bias, fw_projection_weights, fw_projection_bias, bw_input_to_input_weights, bw_input_to_forget_weights, bw_input_to_cell_weights, bw_input_to_output_weights, bw_recurrent_to_input_weights, bw_recurrent_to_forget_weights, bw_recurrent_to_cell_weights, bw_recurrent_to_output_weights, bw_cell_to_input_weights, bw_cell_to_forget_weights, bw_cell_to_output_weights, bw_input_gate_bias, bw_forget_gate_bias, bw_cell_bias, bw_output_gate_bias, bw_projection_weights, bw_projection_bias, fw_activation_state, fw_cell_state, bw_activation_state, bw_cell_state, aux_input, fw_aux_input_to_input_weights, fw_aux_input_to_forget_weights, fw_aux_input_to_cell_weights, fw_aux_input_to_output_weights, bw_aux_input_to_input_weights, bw_aux_input_to_forget_weights, bw_aux_input_to_cell_weights, bw_aux_input_to_output_weights, activation, cell_clip, proj_clip, merge_outputs, time_major, fw_input_layer_norm_weights, fw_forget_layer_norm_weights, fw_cell_layer_norm_weights, fw_output_layer_norm_weights, bw_input_layer_norm_weights, bw_forget_layer_norm_weights, bw_cell_layer_norm_weights, bw_output_layer_norm_weights, ).To(fw_output, bw_output).IntroducedIn("V1_2") example = Example( { input: input_data, fw_input_to_input_weights: fw_input_to_input_weights_data, fw_input_to_forget_weights: fw_input_to_forget_weights_data, fw_input_to_cell_weights: fw_input_to_cell_weights_data, fw_input_to_output_weights: fw_input_to_output_weights_data, fw_recurrent_to_input_weights: fw_recurrent_to_input_weights_data, fw_recurrent_to_forget_weights: fw_recurrent_to_forget_weights_data, fw_recurrent_to_cell_weights: fw_recurrent_to_cell_weights_data, fw_recurrent_to_output_weights: fw_recurrent_to_output_weights_data, fw_cell_to_input_weights: fw_cell_to_input_weights_data, fw_cell_to_forget_weights: fw_cell_to_forget_weights_data, fw_cell_to_output_weights: fw_cell_to_output_weights_data, fw_input_gate_bias: fw_input_gate_bias_data, fw_forget_gate_bias: fw_forget_gate_bias_data, fw_cell_bias: fw_cell_bias_data, fw_output_gate_bias: fw_output_gate_bias_data, fw_projection_weights: fw_projection_weights_data, fw_projection_bias: fw_projection_bias_data, bw_input_to_input_weights: bw_input_to_input_weights_data, bw_input_to_forget_weights: bw_input_to_forget_weights_data, bw_input_to_cell_weights: bw_input_to_cell_weights_data, bw_input_to_output_weights: bw_input_to_output_weights_data, bw_recurrent_to_input_weights: bw_recurrent_to_input_weights_data, bw_recurrent_to_forget_weights: bw_recurrent_to_forget_weights_data, bw_recurrent_to_cell_weights: bw_recurrent_to_cell_weights_data, bw_recurrent_to_output_weights: bw_recurrent_to_output_weights_data, bw_cell_to_input_weights: bw_cell_to_input_weights_data, bw_cell_to_forget_weights: bw_cell_to_forget_weights_data, bw_cell_to_output_weights: bw_cell_to_output_weights_data, bw_input_gate_bias: bw_input_gate_bias_data, bw_forget_gate_bias: bw_forget_gate_bias_data, bw_cell_bias: bw_cell_bias_data, bw_output_gate_bias: bw_output_gate_bias_data, bw_projection_weights: bw_projection_weights_data, bw_projection_bias: bw_projection_bias_data, fw_activation_state: fw_activation_state_data, fw_cell_state: fw_cell_state_data, bw_activation_state: bw_activation_state_data, bw_cell_state: bw_cell_state_data, aux_input: aux_input_data, fw_aux_input_to_input_weights: fw_aux_input_to_input_weights_data, fw_aux_input_to_forget_weights: fw_aux_input_to_forget_weights_data, fw_aux_input_to_cell_weights: fw_aux_input_to_cell_weights_data, fw_aux_input_to_output_weights: fw_aux_input_to_output_weights_data, bw_aux_input_to_input_weights: bw_aux_input_to_input_weights_data, bw_aux_input_to_forget_weights: bw_aux_input_to_forget_weights_data, bw_aux_input_to_cell_weights: bw_aux_input_to_cell_weights_data, bw_aux_input_to_output_weights: bw_aux_input_to_output_weights_data, fw_input_layer_norm_weights: fw_input_layer_norm_weights_data, fw_forget_layer_norm_weights: fw_forget_layer_norm_weights_data, fw_cell_layer_norm_weights: fw_cell_layer_norm_weights_data, fw_output_layer_norm_weights: fw_output_layer_norm_weights_data, bw_input_layer_norm_weights: bw_input_layer_norm_weights_data, bw_forget_layer_norm_weights: bw_forget_layer_norm_weights_data, bw_cell_layer_norm_weights: bw_cell_layer_norm_weights_data, bw_output_layer_norm_weights: bw_output_layer_norm_weights_data, fw_output: fw_output_data, bw_output: bw_output_data, }, model=model, name=name) fw_input_to_input_weights_data = [ -0.45018822, -0.02338299, -0.0870589, -0.34550029, 0.04266912, -0.15680569, -0.34856534, 0.43890524 ] bw_input_to_input_weights_data = fw_input_to_input_weights_data fw_input_to_forget_weights_data = [ 0.09701663, 0.20334584, -0.50592935, -0.31343272, -0.40032279, 0.44781327, 0.01387155, -0.35593212 ] bw_input_to_forget_weights_data = fw_input_to_forget_weights_data fw_input_to_cell_weights_data = [ -0.50013041, 0.1370284, 0.11810488, 0.2013163, -0.20583314, 0.44344562, 0.22077113, -0.29909778 ] bw_input_to_cell_weights_data = fw_input_to_cell_weights_data fw_input_to_output_weights_data = [ -0.25065863, -0.28290087, 0.04613829, 0.40525138, 0.44272184, 0.03897077, -0.1556896, 0.19487578 ] bw_input_to_output_weights_data = fw_input_to_output_weights_data fw_recurrent_to_input_weights_data = [ -0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324, -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322, -0.12528998, 0.24077177, -0.51332325, -0.33502164, 0.10629296 ] bw_recurrent_to_input_weights_data = fw_recurrent_to_input_weights_data fw_recurrent_to_forget_weights_data = [ -0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892, -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436, 0.28053468, 0.01560611, -0.20127171, -0.01140004 ] bw_recurrent_to_forget_weights_data = fw_recurrent_to_forget_weights_data fw_recurrent_to_cell_weights_data = [ -0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841, -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659, -0.46367589, 0.26016325, -0.03894562, -0.16368064 ] bw_recurrent_to_cell_weights_data = fw_recurrent_to_cell_weights_data fw_recurrent_to_output_weights_data = [ 0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793, 0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421, -0.51818722, -0.15390486, 0.0468148, 0.39922136 ] bw_recurrent_to_output_weights_data = fw_recurrent_to_output_weights_data fw_input_gate_bias_data = [0.0, 0.0, 0.0, 0.0] bw_input_gate_bias_data = [0.0, 0.0, 0.0, 0.0] fw_forget_gate_bias_data = [1.0, 1.0, 1.0, 1.0] bw_forget_gate_bias_data = [1.0, 1.0, 1.0, 1.0] fw_cell_bias_data = [0.0, 0.0, 0.0, 0.0] bw_cell_bias_data = [0.0, 0.0, 0.0, 0.0] fw_output_gate_bias_data = [0.0, 0.0, 0.0, 0.0] bw_output_gate_bias_data = [0.0, 0.0, 0.0, 0.0] input_data = [2.0, 3.0, 3.0, 4.0, 1.0, 1.0] fw_golden_output_data = [ -0.02973187, 0.1229473, 0.20885126, -0.15358765, -0.03716109, 0.12507336, 0.41193449, -0.20860538, -0.15053082, 0.09120187, 0.24278517, -0.12222792 ] bw_golden_output_data = [ -0.0806187, 0.139077, 0.400476, -0.197842, -0.0332076, 0.123838, 0.309777, -0.17621, -0.0490733, 0.0739237, 0.067706, -0.0208124 ] n_batch = 1 n_fw_input = 2 n_bw_input = 2 n_cell = 4 n_output = 4 max_time = 3 test( name="parallel_linking", n_batch=n_batch, n_fw_input=n_fw_input, n_bw_input=n_bw_input, n_cell=n_cell, n_output=n_output, max_time=max_time, input_data=input_data, fw_input_to_input_weights_data=fw_input_to_input_weights_data, fw_input_to_forget_weights_data=fw_input_to_forget_weights_data, fw_input_to_cell_weights_data=fw_input_to_cell_weights_data, fw_input_to_output_weights_data=fw_input_to_output_weights_data, fw_recurrent_to_input_weights_data=fw_recurrent_to_input_weights_data, fw_recurrent_to_forget_weights_data=fw_recurrent_to_forget_weights_data, fw_recurrent_to_cell_weights_data=fw_recurrent_to_cell_weights_data, fw_recurrent_to_output_weights_data=fw_recurrent_to_output_weights_data, fw_input_gate_bias_data=fw_input_gate_bias_data, fw_forget_gate_bias_data=fw_forget_gate_bias_data, fw_cell_bias_data=fw_cell_bias_data, fw_output_gate_bias_data=fw_output_gate_bias_data, bw_input_to_input_weights_data=bw_input_to_input_weights_data, bw_input_to_forget_weights_data=bw_input_to_forget_weights_data, bw_input_to_cell_weights_data=bw_input_to_cell_weights_data, bw_input_to_output_weights_data=bw_input_to_output_weights_data, bw_recurrent_to_input_weights_data=bw_recurrent_to_input_weights_data, bw_recurrent_to_forget_weights_data=bw_recurrent_to_forget_weights_data, bw_recurrent_to_cell_weights_data=bw_recurrent_to_cell_weights_data, bw_recurrent_to_output_weights_data=bw_recurrent_to_output_weights_data, bw_input_gate_bias_data=bw_input_gate_bias_data, bw_forget_gate_bias_data=bw_forget_gate_bias_data, bw_cell_bias_data=bw_cell_bias_data, bw_output_gate_bias_data=bw_output_gate_bias_data, fw_activation_state_data=[0] * (n_batch * n_output), bw_activation_state_data=[0] * (n_batch * n_output), fw_cell_state_data=[0] * (n_batch * n_cell), bw_cell_state_data=[0] * (n_batch * n_cell), aux_input_data=input_data, fw_output_data=fw_golden_output_data, bw_output_data=bw_golden_output_data) n_batch = 1 n_fw_input = 3 n_bw_input = 2 n_cell = 4 n_output = 4 max_time = 3 test( name="parallel_linking", n_batch=n_batch, n_fw_input=n_fw_input, n_bw_input=n_bw_input, n_cell=n_cell, n_output=n_output, max_time=max_time, input_data=[0] * (max_time * n_batch * n_fw_input), fw_input_to_input_weights_data=[0] * (n_cell * n_fw_input), fw_input_to_forget_weights_data=[0] * (n_cell * n_fw_input), fw_input_to_cell_weights_data=[0] * (n_cell * n_fw_input), fw_input_to_output_weights_data=[0] * (n_cell * n_fw_input), fw_recurrent_to_input_weights_data=fw_recurrent_to_input_weights_data, fw_recurrent_to_forget_weights_data=fw_recurrent_to_forget_weights_data, fw_recurrent_to_cell_weights_data=fw_recurrent_to_cell_weights_data, fw_recurrent_to_output_weights_data=fw_recurrent_to_output_weights_data, fw_input_gate_bias_data=fw_input_gate_bias_data, fw_forget_gate_bias_data=fw_forget_gate_bias_data, fw_cell_bias_data=fw_cell_bias_data, fw_output_gate_bias_data=fw_output_gate_bias_data, bw_input_to_input_weights_data=bw_input_to_input_weights_data, bw_input_to_forget_weights_data=bw_input_to_forget_weights_data, bw_input_to_cell_weights_data=bw_input_to_cell_weights_data, bw_input_to_output_weights_data=bw_input_to_output_weights_data, bw_recurrent_to_input_weights_data=bw_recurrent_to_input_weights_data, bw_recurrent_to_forget_weights_data=bw_recurrent_to_forget_weights_data, bw_recurrent_to_cell_weights_data=bw_recurrent_to_cell_weights_data, bw_recurrent_to_output_weights_data=bw_recurrent_to_output_weights_data, bw_input_gate_bias_data=bw_input_gate_bias_data, bw_forget_gate_bias_data=bw_forget_gate_bias_data, bw_cell_bias_data=bw_cell_bias_data, bw_output_gate_bias_data=bw_output_gate_bias_data, fw_activation_state_data=[0] * (n_batch * n_output), bw_activation_state_data=[0] * (n_batch * n_output), fw_cell_state_data=[0] * (n_batch * n_cell), bw_cell_state_data=[0] * (n_batch * n_cell), aux_input_data=input_data, fw_output_data=fw_golden_output_data, bw_output_data=bw_golden_output_data, ignore_fw_output=True)