Create model using float 16 data type

Fp16_model.py

# source: https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html
import tensorflow as tf

def create_simple_model(nbatch, nin, nout, dtype):
    """A simple softmax model."""
    data = tf.placeholder(dtype, shape=(nbatch, nin))
    weights = tf.get_variable('weights', (nin, nout), dtype)
    biases = tf.get_variable('biases', nout, dtype, initializer=tf.zeros_initializer())
    logits = tf.matmul(data, weights) + biases
    target = tf.placeholder(tf.float32, shape=(nbatch, nout))
    
    # Note: The softmax should be computed in float32 precision
    loss = tf.losses.softmax_cross_entropy(target, tf.cast(logits, tf.float32))
    
    return data, target, loss