Create README.md

README.md

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+
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+```python
+import tensorflow as tf
+import numpy as np
+import pandas as pd
+import sys
+from sklearn.model_selection import train_test_split
+
+sys.path.insert(0, "bert_experimental")
+
+from bert_experimental.finetuning.text_preprocessing import build_preprocessor
+from bert_experimental.finetuning.graph_ops import load_graph
+
+
+df = pd.read_csv("data/test.tsv", sep='\t')
+
+
+texts = []
+delimiter = " ||| "
+
+for vis, cap  in zip(df.visual.tolist(), df.caption.tolist()):
+  texts.append(delimiter.join((str(vis), str(cap))))
+
+
+texts = np.array(texts)
+
+trX, tsX = train_test_split(texts, shuffle=False, test_size=0.01)
+
+
+restored_graph = load_graph("frozen_graph.pb")
+
+graph_ops = restored_graph.get_operations()
+input_op, output_op = graph_ops[0].name, graph_ops[-1].name
+print(input_op, output_op)
+
+x = restored_graph.get_tensor_by_name(input_op + ':0')
+y = restored_graph.get_tensor_by_name(output_op + ':0')
+
+preprocessor = build_preprocessor("uncased_L-12_H-768_A-12/vocab.txt", 64)
+py_func = tf.numpy_function(preprocessor, [x], [tf.int32, tf.int32, tf.int32], name='preprocessor')
+
+py_func = tf.numpy_function(preprocessor, [x], [tf.int32, tf.int32, tf.int32])
+
+##predictions
+
+sess = tf.Session(graph=restored_graph)
+
+print(trX[:4])
+
+y = tf.print(y, summarize=-1)
+#x = tf.print(x, summarize=-1)
+y_out = sess.run(y, feed_dict={
+        x: trX[:4].reshape((-1,1))
+ 	#x: trX[:90000].reshape((-1,1))
+    })
+
+
+print(y_out)
+
+
+````