Replace tf.log() with tf.math.log() and update arguments to tf.reduce_sum().

Pin to a previous tf-nightly version that is still TF 1.x compatible.

PiperOrigin-RevId: 272734678

Dockerfile

@@ -75,6 +75,6 @@ RUN ${PIP} install --no-cache-dir \
     tensorflow-model-optimization~=0.1.3 \
     tensorflow-privacy~=0.0.1 \
     tf-estimator-nightly==1.14.0.dev2019091601 \
-    tf-nightly \
+    tf-nightly==1.15.0.dev20190821 \
     typing~=3.7.0
 RUN pip freeze

requirements.txt

@@ -17,5 +17,5 @@ tensorflow-model-optimization~=0.1.3
 tensorflow-privacy~=0.0.1
 # TODO(b/141279425): Remove pinned tf-estimator-nightly version.
 tf-estimator-nightly==1.14.0.dev2019091601
-tf-nightly
+tf-nightly==1.15.0.dev20190821
 typing~=3.7.0

tensorflow_federated/python/core/backends/mapreduce/test_utils.py

@@ -193,9 +193,7 @@ def get_mnist_training_example():
             tf.matmul(batch.x, model_vars.weights) + model_vars.bias)
         loss = -tf.reduce_mean(
             tf.reduce_sum(
-                tf.one_hot(batch.y, 10) * tf.log(pred_y),
-                reduction_indices=[1],
-            ))
+                tf.one_hot(batch.y, 10) * tf.math.log(pred_y), axis=[1]))
       grads = tape.gradient(loss, model_vars)
       optimizer.apply_gradients(
           zip(tf.nest.flatten(grads), tf.nest.flatten(model_vars)))

tensorflow_federated/tools/development/setup.py

@@ -84,7 +84,7 @@ REQUIRED_PACKAGES = [
     'tensorflow-privacy~=0.0.1',
     # TODO(b/141279425): Remove pinned tf-estimator-nightly version.
     'tf-estimator-nightly==1.14.0.dev2019091601',
-    'tf-nightly',
+    'tf-nightly==1.15.0.dev20190821',
 ]
 
 setuptools.setup(