change: Make tf tests tf-1.15 and tf-2.0 compatible.

tests/data/tensorflow_mnist/mnist.py

@@ -18,7 +18,7 @@
 import os
 import tensorflow as tf
 
-tf.logging.set_verbosity(tf.logging.DEBUG)
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.DEBUG)
 
 
 def cnn_model_fn(features, labels, mode):
@@ -33,30 +33,30 @@ def cnn_model_fn(features, labels, mode):
     # Padding is added to preserve width and height.
     # Input Tensor Shape: [batch_size, 28, 28, 1]
     # Output Tensor Shape: [batch_size, 28, 28, 32]
-    conv1 = tf.layers.conv2d(
+    conv1 = tf.compat.v1.layers.conv2d(
         inputs=input_layer, filters=32, kernel_size=[5, 5], padding="same", activation=tf.nn.relu
     )
 
     # Pooling Layer #1
     # First max pooling layer with a 2x2 filter and stride of 2
     # Input Tensor Shape: [batch_size, 28, 28, 32]
     # Output Tensor Shape: [batch_size, 14, 14, 32]
-    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
+    pool1 = tf.compat.v1.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
 
     # Convolutional Layer #2
     # Computes 64 features using a 5x5 filter.
     # Padding is added to preserve width and height.
     # Input Tensor Shape: [batch_size, 14, 14, 32]
     # Output Tensor Shape: [batch_size, 14, 14, 64]
-    conv2 = tf.layers.conv2d(
+    conv2 = tf.compat.v1.layers.conv2d(
         inputs=pool1, filters=64, kernel_size=[5, 5], padding="same", activation=tf.nn.relu
     )
 
     # Pooling Layer #2
     # Second max pooling layer with a 2x2 filter and stride of 2
     # Input Tensor Shape: [batch_size, 14, 14, 64]
     # Output Tensor Shape: [batch_size, 7, 7, 64]
-    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
+    pool2 = tf.compat.v1.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
 
     # Flatten tensor into a batch of vectors
     # Input Tensor Shape: [batch_size, 7, 7, 64]
@@ -67,17 +67,17 @@ def cnn_model_fn(features, labels, mode):
     # Densely connected layer with 1024 neurons
     # Input Tensor Shape: [batch_size, 7 * 7 * 64]
     # Output Tensor Shape: [batch_size, 1024]
-    dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
+    dense = tf.compat.v1.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
 
     # Add dropout operation; 0.6 probability that element will be kept
-    dropout = tf.layers.dropout(
+    dropout = tf.compat.v1.layers.dropout(
         inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN
     )
 
     # Logits layer
     # Input Tensor Shape: [batch_size, 1024]
     # Output Tensor Shape: [batch_size, 10]
-    logits = tf.layers.dense(inputs=dropout, units=10)
+    logits = tf.compat.v1.layers.dense(inputs=dropout, units=10)
 
     predictions = {
         # Generate predictions (for PREDICT and EVAL mode)
@@ -90,17 +90,17 @@ def cnn_model_fn(features, labels, mode):
         return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
 
     # Calculate Loss (for both TRAIN and EVAL modes)
-    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
+    loss = tf.compat.v1.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
 
     # Configure the Training Op (for TRAIN mode)
     if mode == tf.estimator.ModeKeys.TRAIN:
-        optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
-        train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())
+        optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.001)
+        train_op = optimizer.minimize(loss=loss, global_step=tf.compat.v1.train.get_global_step())
         return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
 
     # Add evaluation metrics (for EVAL mode)
     eval_metric_ops = {
-        "accuracy": tf.metrics.accuracy(labels=labels, predictions=predictions["classes"])
+        "accuracy": tf.compat.v1.metrics.accuracy(labels=labels, predictions=predictions["classes"])
     }
     return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
 
@@ -134,7 +134,7 @@ def _parse_args():
 
 
 def serving_input_fn():
-    inputs = {"x": tf.placeholder(tf.float32, [None, 784])}
+    inputs = {"x": tf.compat.v1.placeholder(tf.float32, [None, 784])}
     return tf.estimator.export.ServingInputReceiver(inputs, inputs)
 
 
@@ -155,15 +155,15 @@ def serving_input_fn():
     # Set up logging for predictions
     # Log the values in the "Softmax" tensor with label "probabilities"
     tensors_to_log = {"probabilities": "softmax_tensor"}
-    logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=50)
+    logging_hook = tf.estimator.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=50)
 
     # Train the model
-    train_input_fn = tf.estimator.inputs.numpy_input_fn(
+    train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
         x={"x": train_data}, y=train_labels, batch_size=50, num_epochs=None, shuffle=True
     )
 
     # Evaluate the model and print results
-    eval_input_fn = tf.estimator.inputs.numpy_input_fn(
+    eval_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
         x={"x": eval_data}, y=eval_labels, num_epochs=1, shuffle=False
     )
 
@@ -172,4 +172,4 @@ def serving_input_fn():
     tf.estimator.train_and_evaluate(mnist_classifier, train_spec, eval_spec)
 
     if args.current_host == args.hosts[0]:
-        mnist_classifier.export_savedmodel("/opt/ml/model", serving_input_fn)
+        mnist_classifier.export_saved_model("/opt/ml/model", serving_input_fn)