/external/tensorflow/tensorflow/python/keras/layers/ |
lstm_test.py | 39 embedding_dim = 4 45 input_shape=(num_samples, timesteps, embedding_dim)) 50 embedding_dim = 4 54 inputs = keras.layers.Dense(embedding_dim, 55 input_shape=(timesteps, embedding_dim)) 65 embedding_dim = 4 67 layer = keras.layers.LSTM(units, input_shape=(None, embedding_dim)) 73 x = np.random.random((num_samples, timesteps, embedding_dim)) 80 embedding_dim = 4 87 input_shape=(num_samples, timesteps, embedding_dim)) [all...] |
gru_test.py | 38 embedding_dim = 4 44 input_shape=(num_samples, timesteps, embedding_dim)) 49 embedding_dim = 4 51 layer = keras.layers.GRU(units, input_shape=(None, embedding_dim)) 56 x = np.random.random((num_samples, timesteps, embedding_dim)) 63 embedding_dim = 4 70 input_shape=(num_samples, timesteps, embedding_dim)) 76 embedding_dim = 4 82 input_shape=(num_samples, timesteps, embedding_dim)) 87 embedding_dim = [all...] |
simplernn_test.py | 37 embedding_dim = 4 43 input_shape=(num_samples, timesteps, embedding_dim)) 48 embedding_dim = 4 50 layer = keras.layers.SimpleRNN(units, input_shape=(None, embedding_dim)) 54 x = np.random.random((num_samples, timesteps, embedding_dim)) 61 embedding_dim = 4 68 input_shape=(num_samples, timesteps, embedding_dim)) 73 embedding_dim = 4 80 input_shape=(num_samples, timesteps, embedding_dim)) 83 embedding_dim = [all...] |
lstm_v2_test.py | 83 embedding_dim = 4 88 embedding_dim, input_shape=(timesteps, embedding_dim)) 98 embedding_dim = 4 100 layer = rnn.LSTM(units, input_shape=(None, embedding_dim)) 104 x = np.random.random((num_samples, timesteps, embedding_dim)) 130 embedding_dim = 4 135 inputs = keras.Input((timesteps, embedding_dim)) 149 inputs = np.random.random((num_samples, timesteps, embedding_dim)) 159 embedding_dim = [all...] |
recurrent_v2_test.py | 44 embedding_dim = 10 55 keras.layers.Embedding(vocab_size, embedding_dim,
|
gru_v2_test.py | 111 embedding_dim = 4 113 layer = rnn.GRU(units, input_shape=(None, embedding_dim)) 117 x = np.random.random((num_samples, timesteps, embedding_dim)) 332 embedding_dim = 4 338 input_shape=(num_samples, timesteps, embedding_dim)) 358 embedding_dim = 4 365 input_shape=(num_samples, timesteps, embedding_dim)) 368 embedding_dim = 4 377 input_shape=(None, embedding_dim), 381 layer.build((None, None, embedding_dim)) [all...] |
recurrent_test.py | 284 embedding_dim = 4 288 x = keras.Input((time_step, embedding_dim)) 295 embedding_dim)).as_list(), 308 np.zeros((batch, time_step, embedding_dim)), 312 x = keras.Input((time_step, embedding_dim)) 328 np.zeros((batch, time_step, embedding_dim)), 332 x = keras.Input((time_step, embedding_dim)) 345 np.zeros((batch, time_step, embedding_dim)), 349 x = keras.Input((time_step, embedding_dim)) 359 np.zeros((batch, time_step, embedding_dim)), [all...] |
/external/libtextclassifier/lang_id/common/ |
embedding-network.cc | 159 const int embedding_dim = embedding_matrix.cols; local 167 int feature_offset = concat_offset + feature_type->base() * embedding_dim; 168 SAFTM_CHECK_LE(feature_offset + embedding_dim, concat->size()); 201 for (int i = 0; i < embedding_dim; ++i, ++weights, ++concat_ptr) { 210 for (int i = 0; i < embedding_dim; 222 for (int i = 0; i < embedding_dim / 2; ++i, ++quant_weights) {
|
/external/tensorflow/tensorflow/contrib/eager/python/examples/rnn_ptb/ |
rnn_ptb.py | 81 def __init__(self, vocab_size, embedding_dim, **kwargs): 84 self.embedding_dim = embedding_dim 89 shape=[self.vocab_size, self.embedding_dim], 112 embedding_dim, 121 self.embedding = Embedding(vocab_size, embedding_dim) 269 embedding_dim=200, 280 embedding_dim=650, 291 embedding_dim=20, 314 model = PTBModel(corpus.vocab_size(), FLAGS.embedding_dim, [all...] |
/external/tensorflow/tensorflow/python/training/ |
checkpoint_ops.py | 422 embedding_dim, 442 embedding_dim: `int` specifying the dimension of the embedding vectors from 471 stddev=1.0 / math.sqrt(embedding_dim)) 477 new_col_vocab_size=embedding_dim,
|
checkpoint_ops_test.py | 275 embedding_dim=16, 320 embedding_dim=16, 359 embedding_dim=16,
|
/external/tensorflow/tensorflow/contrib/seq2seq/python/kernel_tests/ |
beam_search_decoder_test.py | 490 embedding_dim = 50 497 embedding = np.random.randn(vocab_size, embedding_dim).astype(np.float32) 604 embedding_dim = 50 611 embedding = np.random.randn(vocab_size, embedding_dim).astype(np.float32)
|
attention_wrapper_v2_test.py | 133 embedding_dim = 6 136 vocab, embedding_dim, mask_zero=True)(
|
/external/tensorflow/tensorflow/python/keras/ |
model_subclassing_test.py | 252 def __init__(self, vocab_size, embedding_dim, **kwargs): 255 self.embedding_dim = embedding_dim 260 shape=[self.vocab_size, self.embedding_dim], [all...] |
/external/tensorflow/tensorflow/python/grappler/ |
hierarchical_controller.py | 555 embedding_dim = array_ops.shape(input_layer)[2] 558 [batch_size * self.num_ops, embedding_dim]) [all...] |