| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | lstm_test.py | 38 timesteps = 3
45 input_shape=(num_samples, timesteps, embedding_dim))
49 timesteps = 3
55 input_shape=(timesteps, embedding_dim))
60 self.assertEqual(outputs.get_shape().as_list(), [None, timesteps, units])
64 timesteps = 3
73 x = np.random.random((num_samples, timesteps, embedding_dim))
79 timesteps = 3
87 input_shape=(num_samples, timesteps, embedding_dim))
92 timesteps = 3
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| D | gru_test.py | 37 timesteps = 3
44 input_shape=(num_samples, timesteps, embedding_dim))
48 timesteps = 3
56 x = np.random.random((num_samples, timesteps, embedding_dim))
62 timesteps = 3
70 input_shape=(num_samples, timesteps, embedding_dim))
75 timesteps = 3
82 input_shape=(num_samples, timesteps, embedding_dim))
86 timesteps = 3
93 input_shape=(timesteps, embedding_dim),
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| D | cudnn_recurrent_test.py | 46 timesteps = 6
53 input_shape=(num_samples, timesteps, input_size))
62 timesteps = 6
69 input_shape=(num_samples, timesteps, input_size))
78 timesteps = 6
83 inputs = keras.Input(batch_shape=(num_samples, timesteps, input_size))
91 inputs = np.random.random((num_samples, timesteps, input_size))
103 timesteps = 6
117 np.ones((num_samples, timesteps, input_size)),
118 np.ones((num_samples, timesteps, units)))
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| D | simplernn_test.py | 36 timesteps = 3
43 input_shape=(num_samples, timesteps, embedding_dim))
47 timesteps = 3
54 x = np.random.random((num_samples, timesteps, embedding_dim))
60 timesteps = 3
68 input_shape=(num_samples, timesteps, embedding_dim))
72 timesteps = 3
80 input_shape=(num_samples, timesteps, embedding_dim))
143 timesteps = 3
153 input_length=timesteps,
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| D | lstm_v2_test.py | 82 timesteps = 3
88 embedding_dim, input_shape=(timesteps, embedding_dim))
93 self.assertEqual(outputs.get_shape().as_list(), [None, timesteps, units])
97 timesteps = 3
104 x = np.random.random((num_samples, timesteps, embedding_dim))
129 timesteps = 3
135 inputs = keras.Input((timesteps, embedding_dim))
149 inputs = np.random.random((num_samples, timesteps, embedding_dim))
158 timesteps = 3
164 inputs = keras.Input((timesteps, embedding_dim))
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| D | wrappers_test.py | 301 timesteps = 2
305 x = np.random.random((samples, timesteps, dim))
313 rnn(output_dim), merge_mode=mode, input_shape=(timesteps, dim)))
327 (None, timesteps, dim))
346 timesteps = 2
349 x = np.random.random((samples, timesteps, dim))
353 rnn(output_dim), input_shape=(timesteps, dim)))
365 timesteps = 2
370 x = np.random.random((samples, timesteps, dim))
379 input_shape=(timesteps, dim)))
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| D | gru_v2_test.py | 110 timesteps = 3
117 x = np.random.random((num_samples, timesteps, embedding_dim))
331 timesteps = 3
338 input_shape=(num_samples, timesteps, embedding_dim))
357 timesteps = 3
365 input_shape=(num_samples, timesteps, embedding_dim))
389 timesteps = 3
396 input_shape=(num_samples, timesteps, embedding_dim))
422 timesteps = 3
432 input_length=timesteps,
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| D | recurrent_v2.py | 219 timesteps = input_shape[0] if self.time_major else input_shape[1]
236 input_length=timesteps,
354 timesteps = input_shape[0] if time_major else input_shape[1]
388 input_length=timesteps)
590 timesteps = input_shape[0] if self.time_major else input_shape[1]
607 input_length=timesteps,
754 timesteps = input_shape[0] if time_major else input_shape[1]
781 input_length=timesteps)
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| D | recurrent_test.py | 664 timesteps = 2
667 input1 = keras.Input(batch_shape=(num_samples, timesteps, embedding_dim))
674 input2 = keras.Input(batch_shape=(num_samples, timesteps, embedding_dim))
678 inputs = [np.random.random((num_samples, timesteps, embedding_dim)),
679 np.random.random((num_samples, timesteps, embedding_dim))]
808 timesteps = 2
810 output_shape = layer.compute_output_shape((None, timesteps, embedding_dim))
811 expected_output_shape = [(None, timesteps, 6),
825 output_shape = layer.compute_output_shape((None, timesteps, embedding_dim))
826 expected_output_shape = [(None, timesteps, 6),
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| D | wrappers.py | 215 timesteps = input_shape[1]
216 return tensor_shape.TensorShape([child_output_shape[0], timesteps] +
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| D | convolutional_recurrent.py | 368 timesteps = K.int_shape(inputs)[1]
393 input_length=timesteps)
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| D | recurrent.py | 711 timesteps = input_shape[0] if self.time_major else input_shape[1]
712 if self.unroll and timesteps is None:
762 input_length=timesteps,
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| /external/tensorflow/tensorflow/core/util/ctc/ |
| D | ctc_beam_search_test.cc | 105 const int timesteps = 5; in TEST() local
119 int sequence_lengths[batch_size] = {timesteps}; in TEST()
120 float input_data_mat[timesteps][batch_size][num_classes] = { in TEST()
128 for (int t = 0; t < timesteps; ++t) { in TEST()
153 inputs.reserve(timesteps); in TEST()
154 for (int t = 0; t < timesteps; ++t) { in TEST()
185 const int timesteps = 1; in TEST() local
194 int sequence_lengths[batch_size] = {timesteps}; in TEST()
195 float input_data_mat[timesteps][batch_size][num_classes] = { in TEST()
203 inputs.reserve(timesteps); in TEST()
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| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | training_test.py | 1271 timesteps = 3
1279 input_shape=(timesteps, input_dim)))
1300 temporal_x_train = np.repeat(temporal_x_train, timesteps, axis=1)
1302 temporal_x_test = np.repeat(temporal_x_test, timesteps, axis=1)
1306 temporal_y_train = np.repeat(temporal_y_train, timesteps, axis=1)
1308 temporal_y_test = np.repeat(temporal_y_test, timesteps, axis=1)
1313 temporal_sample_weight, timesteps, axis=1)
1359 timesteps = 3
1367 input_shape=(timesteps, input_dim)))
1431 timesteps = 3
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| /external/tensorflow/tensorflow/python/keras/ |
| D | backend_test.py | 1007 timesteps = 6
1010 (num_samples, timesteps, input_dim)).astype(np.float32)
1015 np_mask = np.random.randint(2, size=(num_samples, timesteps))
1054 [num_samples, timesteps, output_dim])
1097 timesteps = 6
1100 (num_samples, timesteps, input_dim)).astype(np.float32)
1105 np_mask = np.random.randint(2, size=(num_samples, timesteps))
1150 [num_samples, timesteps, output_dim])
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| D | model_subclassing_test.py | 287 timesteps = 1
288 batch_input_shape = (None, timesteps, dim)
308 model(array_ops.ones((32, timesteps, dim)))
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| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
| D | state_space_model.py | 985 def get_features_for_timesteps(self, timesteps): argument
987 return array_ops.zeros([array_ops.shape(timesteps)[0], 0], dtype=self.dtype)
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | conv_ops_test.py | 2698 timesteps = 600
2702 [batch_size, 1, timesteps, features], seed=1234)
|