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@@ -72,10 +72,10 @@ flags.DEFINE_string("output_base_path", None,
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flags.DEFINE_integer("embedding_size", 300, "Size of the embeddings")
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flags.DEFINE_boolean("trainable_bias", False, "Biases are trainable")
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flags.DEFINE_integer("submatrix_rows", 4096,
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- "Rows in each training submatrix. This must match"
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+ "Rows in each training submatrix. This must match "
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"the training data.")
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flags.DEFINE_integer("submatrix_cols", 4096,
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- "Rows in each training submatrix. This must match"
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+ "Rows in each training submatrix. This must match "
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"the training data.")
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flags.DEFINE_float("loss_multiplier", 1.0 / 4096,
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"constant multiplier on loss.")
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@@ -205,9 +205,9 @@ class SwivelModel:
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# Create paths to input data files
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log("Reading model from: %s", config.input_base_path)
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- count_matrix_files = glob.glob(config.input_base_path + "/shard-*.pb")
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- row_sums_path = config.input_base_path + "/row_sums.txt"
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- col_sums_path = config.input_base_path + "/col_sums.txt"
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+ count_matrix_files = glob.glob(os.path.join(config.input_base_path, "shard-*.pb"))
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+ row_sums_path = os.path.join(config.input_base_path, "row_sums.txt")
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+ col_sums_path = os.path.join(config.input_base_path, "col_sums.txt")
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# Read marginals
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row_sums = read_marginals_file(row_sums_path)
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@@ -367,6 +367,8 @@ class SwivelModel:
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tf.zeros((length, self._config.embedding_size)),
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name="top10k_embedding")
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embedding_config.tensor_name = self.embedding10k.name
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+ embedding_config.metadata_path = os.path.join(
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+ self._config.input_base_path, "row_vocab.txt")
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tf.contrib.tensorboard.plugins.projector.visualize_embeddings(
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self.writer, projector_config)
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self.saver = tf.train.Saver((self.embedding10k,), max_to_keep=1)
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Vadim Markovtsev