1 files changed, 65 insertions, 0 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
new file mode 100644
index 00000000000..7aae61b49a7
--- /dev/null
+++ b/model-integration/src/test/models/tensorflow/external/train_embed.py
@@ -0,0 +1,65 @@
+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()