PrettyTensor allows the developer to wrap TensorFlow operations to quickly chain any number of layers to define neural networks. Coming up is simple example of Pretty Tensor's capabilities: we wrap a standard TensorFlow object, pretty, into a library-compatible object; then we feed it through three fully connected layers, and we finally output a softmax distribution:
pretty = tf.placeholder([None, 784], tf.float32) softmax = (prettytensor.wrap(examples) .fully_connected(256, tf.nn.relu) .fully_connected(128, tf.sigmoid) .fully_connected(64, tf.tanh) .softmax(10))
The PrettyTensor installation is very simple. You can just use the
sudo pip install prettytensor
PrettyTensor has three modes of operation that ...