Updates docs to refer to TF 1.15.

Also enables explicit convolution mode by default, since it is now supported.

PiperOrigin-RevId: 281167413
Change-Id: I96bccf31fa405db23fe8c06cfd6543da7998be72

Author: Johannes Ball?

README.md

@@ -50,15 +50,17 @@ packages using the `pip` command. Refer to the
 [TensorFlow installation instructions](https://www.tensorflow.org/install/pip)
 for more information on how to set up such a Python environment.
 
-The current version of tensorflow-compression requires TensorFlow 1.14 or
-above. You can install TensorFlow from any source. To install it via `pip`, run
-the following command:
+The current version of tensorflow-compression requires TensorFlow 1.15. For
+TensorFlow 1.14 or earlier, see our
+[previous releases](https://github.com/tensorflow/compression/releases). You can
+install TensorFlow from any source. To install it via `pip`, run the following
+command:
 ```bash
-pip install tensorflow-gpu
+pip install tensorflow-gpu==1.15
 ```
 for GPU support, or
 ```bash
-pip install tensorflow
+pip install tensorflow==1.15
 ```
 for CPU-only.
 
@@ -72,7 +74,7 @@ To test that the installation works correctly, you can run the unit tests with
 ```bash
 python -m tensorflow_compression.python.all_test
 ```
-Once the command finishes, you should see a message ```OK (skipped=11)``` or
+Once the command finishes, you should see a message ```OK (skipped=12)``` or
 similar in the last line.
 
 ### Docker
@@ -102,7 +104,7 @@ and tensorflow-compression with GPU support:
 ```bash
 conda create --name ENV_NAME python=3.6 cudatoolkit=10.0 cudnn
 conda activate ENV_NAME
-pip install tensorflow-gpu tensorflow-compression
+pip install tensorflow-gpu==1.15 tensorflow-compression
 ```
 
 ## Usage

tensorflow_compression/python/layers/signal_conv.py

@@ -192,7 +192,7 @@ def __init__(self, filters, kernel_support,
                corr=False, strides_down=1, strides_up=1, padding="valid",
                extra_pad_end=True, channel_separable=False,
                data_format="channels_last",
-               activation=None, use_bias=False, use_explicit=False,
+               activation=None, use_bias=False, use_explicit=True,
                kernel_initializer=tf.initializers.variance_scaling(),
                bias_initializer=tf.initializers.zeros(),
                kernel_regularizer=None, bias_regularizer=None,