hiranumn / integratedgradientstf Goto Github PK

x_pure = np.random.randint(-10,10,[10, 227, 227, 11])
y_test = np.sum(x_pure**2,axis=(1,2,3))

x = tf.placeholder(tf.float32, name='x_input', shape=[None, 227, 227, 11])
y = tf.placeholder(tf.float32, name='y_input', shape=[None, 1])

res = ig.build_ig(inter, stepsize, pred, num_steps=50)

def build_ig(samples, stepsizes, _output, num_steps=50):
    grads = tf.gradients(ys=_output, xs=samples)

_output=Tensor("Sum:0", shape=(?,), dtype=float32)
samples=Tensor("Reshape:0", shape=(?, 227, 227, 11), dtype=float32)
grads=<class 'list'>: [None]