for the given datapoints in a list original_gradients_list=list(backward_dict.values())# you can use reverse function if the values are in reverse order approx_gradients_list=[] w = np.ones(9)*0.1
#now we have to write code for approx gr
) let loss = tf.reduce_sum(tf.pow(h - y,2)) / (2 * m) optimizer.apply_gradients(zip(gradients, struct (b,W)))let h = tf.matmul(x, W)
let loss = tf.reduce_sum(tf.pow
tf.train.AdamOptimizer(0.001).minimize(self.c_loss, var_list=self.ce_params)dq_da = tf.gradientselse, lower methodgrad = tf.gradientsapply gradient to the parameters in actor network
sel