AhmedSSabir commited on
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Update README.md

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  1. README.md +1 -9
README.md CHANGED
@@ -26,11 +26,7 @@ sys.path.insert(0, "bert_experimental")
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  from bert_experimental.finetuning.text_preprocessing import build_preprocessor
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  from bert_experimental.finetuning.graph_ops import load_graph
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-
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  df = pd.read_csv("test.tsv", sep='\t')
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- # visual information image caption
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- # standard poodle shopping cart footwear a close up of a dog laying in a basket
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-
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  texts = []
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  delimiter = " ||| "
@@ -38,12 +34,10 @@ delimiter = " ||| "
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  for vis, cap in zip(df.visual.tolist(), df.caption.tolist()):
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  texts.append(delimiter.join((str(vis), str(cap))))
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-
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  texts = np.array(texts)
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  trX, tsX = train_test_split(texts, shuffle=False, test_size=0.01)
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-
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  restored_graph = load_graph("frozen_graph.pb")
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  graph_ops = restored_graph.get_operations()
@@ -59,16 +53,14 @@ py_func = tf.numpy_function(preprocessor, [x], [tf.int32, tf.int32, tf.int32], n
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  py_func = tf.numpy_function(preprocessor, [x], [tf.int32, tf.int32, tf.int32])
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  ##predictions
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-
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  sess = tf.Session(graph=restored_graph)
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  print(trX[:4])
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  y = tf.print(y, summarize=-1)
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- #x = tf.print(x, summarize=-1)
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  y_out = sess.run(y, feed_dict={
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  x: trX[:4].reshape((-1,1))
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- #x: trX[:90000].reshape((-1,1))
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  })
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  from bert_experimental.finetuning.text_preprocessing import build_preprocessor
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  from bert_experimental.finetuning.graph_ops import load_graph
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  df = pd.read_csv("test.tsv", sep='\t')
 
 
 
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  texts = []
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  delimiter = " ||| "
 
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  for vis, cap in zip(df.visual.tolist(), df.caption.tolist()):
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  texts.append(delimiter.join((str(vis), str(cap))))
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  texts = np.array(texts)
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  trX, tsX = train_test_split(texts, shuffle=False, test_size=0.01)
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  restored_graph = load_graph("frozen_graph.pb")
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  graph_ops = restored_graph.get_operations()
 
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  py_func = tf.numpy_function(preprocessor, [x], [tf.int32, tf.int32, tf.int32])
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  ##predictions
 
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  sess = tf.Session(graph=restored_graph)
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  print(trX[:4])
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  y = tf.print(y, summarize=-1)
 
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  y_out = sess.run(y, feed_dict={
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  x: trX[:4].reshape((-1,1))
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+
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  })
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