def forward(self, x):
"""Performs the forward pass.
Args:
x: 2-d array of size batch_size x image_size.
Returns:
A 2-d array of size batch_size x num_classes.
"""
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-x))
for w, b in zip(self.weights, self.biases):
x = sigmoid(np.dot(w, x.T).T + b)
return x
def foo(tree_arg):
x, (y, z) = tree_arg
return tf_np.dot(x, tf_np.dot(y, z))
def foo2(tree_arg):
x, dct = tree_arg
y, z = dct['a'], dct['b']
return tf_np.dot(x, tf_np.dot(y, z))
