notebook.community

Edit and run



In [11]:

    
import tangent



In [2]:

    
import tensorflow as tf



In [3]:

    
def f(W,x):
  h1 = tf.matmul(x,W)
  h2 = tf.tanh(h1)
  out = tf.reduce_sum(h2)
  return out



In [4]:

    
dfdW = tangent.grad(f)



In [5]:

    
dfdW??

output:

def dfdW(W, x, bout=1.0):
    h1 = tf.matmul(x, W)
    h2 = tf.tanh(h1)

    # Grad of: out = tf.reduce_sum(h2)
    _bh2 = tangent.unreduce(bout, tangent.shape_as_list(h2), None, None)
    bh2 = _bh2

    # Grad of: h2 = tf.tanh(h1)
    _h2 = h2
    _bh1 = bh2 * (1 - _h2 * _h2)
    bh1 = _bh1

    # Grad of: h1 = tf.matmul(x, W)
    _bW = tangent.matmul_adjoint_y(bh1, x, W, False, False)
    bW = _bW
    return bW

okay this is pretty cool



In [6]:

    
import numpy as np



In [7]:

    
# See here:
# https://github.com/google/tangent/blob/6f19d40f333c9b73a9cfd6a9bce78d7fb5dfd035/tangent/grads.py#L212-L298

def np_f(W,x):
  h1 = np.multiply(x,W)
  h2 = np.tanh(h1)
  out = np.sum(h2)
  return out



In [9]:

    
np_dfdW = tangent.grad(np_f)



In [10]:

    
np_dfdW??

def dnp_fdW(W, x, bout=1.0):
    h1 = np.multiply(x, W)
    h2 = np.tanh(h1)

    # Grad of: out = np.sum(h2)
    _bh2 = tangent.astype(tangent.unreduce(bout, numpy.shape(h2), None, np.
        _NoValue), h2)
    bh2 = _bh2

    # Grad of: h2 = np.tanh(h1)
    _h2 = h2
    _bh1 = bh2 * (1.0 - _h2 * _h2)
    bh1 = _bh1

    # Grad of: h1 = np.multiply(x, W)
    _bW = x * bh1
    bW = _bW
    return bW

:tada:



In [ ]: