1 files changed, 0 insertions, 66 deletions
diff --git a/model-integration/src/test/models/tensorflow/external/train_embed.py b/model-integration/src/test/models/tensorflow/external/train_embed.py
deleted file mode 100644
index 2d1ab18a0eb..00000000000
--- a/model-integration/src/test/models/tensorflow/external/train_embed.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# Copyright 2020 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-import numpy as np
-import tensorflow as tf
-import tensorflow.keras.backend as K
-
-from tensorflow.keras.layers import Input, Dense, concatenate, Embedding, Reshape
-from tensorflow.keras.models import Model
-
-input_user = Input(shape=(3,))
-input_ad = Input(shape=(3,))
-gender_samples = Input(shape=(1,), dtype='int32')
-
-gender_values = ['m', 'f', 'a']
-
-gender_embeddings = Embedding(len(gender_values), 1)(gender_samples)
-reshape_gender = Reshape(target_shape=[1])(gender_embeddings)
-
-model2 = Model(inputs=[gender_samples], outputs=reshape_gender)
-model2.summary()
-
-merged = concatenate([input_user, input_ad, reshape_gender])
-output_1 = Dense(64, activation='relu')(merged)
-output_2 = Dense(64, activation='relu')(output_1)
-predictions = Dense(1)(output_2)
-
-model = Model(inputs=[input_user, input_ad, gender_samples], outputs=predictions)
-model.compile(optimizer='adam',
-              loss='binary_crossentropy',
-              metrics=['accuracy'])
-model.summary()
-
-SAMPLES = 1000
-user_data = np.random.rand(SAMPLES,3)
-ad_data = np.random.rand(SAMPLES,3)
-gender_data = np.random.randint(len(gender_values), size=SAMPLES)
-labels = np.random.rand(SAMPLES,1)
-print(user_data[:10])
-print(ad_data[:10])
-print(gender_data[:10])
-print(labels[:10])
-
-model.fit([user_data, ad_data, gender_data], labels, epochs=10, )  # starts training
-
-user_data_sample1 = np.random.rand(1, 3)
-ad_data_sample1 = np.random.rand(1, 3)
-gender_data_sample1 = np.random.randint(len(gender_values), size=1)
-
-print("predicting for:")
-print(user_data_sample1)
-print(ad_data_sample1)
-print(gender_data_sample1)
-predictions = model.predict([user_data_sample1, ad_data_sample1, gender_data_sample1])
-print(predictions)
-
-signature = tf.saved_model.signature_def_utils.predict_signature_def(
-    inputs={'input1': model.inputs[0],'input2': model.inputs[1], 'input3': model.inputs[2] }, outputs={'pctrx': model.outputs[0]})
-
-builder = tf.saved_model.builder.SavedModelBuilder('modelv2')
-builder.add_meta_graph_and_variables(
-    sess=K.get_session(),
-    tags=[tf.saved_model.tag_constants.SERVING],
-    signature_def_map={
-        tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
-            signature
-    })
-builder.save()