import numpy as np
from gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from layers import *

# Test the affine_backward function

x = np.random.randn(10, 2, 3)
w = np.random.randn(6, 5)
b = np.random.randn(5)
dout = np.random.randn(10, 5)

dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)

6b727b8d066f9f243c8b2d98ae7789c1a9761fa7
<mask token>

<mask token>
x = np.random.randn(10, 2, 3)
w = np.random.randn(6, 5)
b = np.random.randn(5)
dout = np.random.randn(10, 5)
dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0],
    x, dout)

import numpy as np
from gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from layers import *
x = np.random.randn(10, 2, 3)
w = np.random.randn(6, 5)
b = np.random.randn(5)
dout = np.random.randn(10, 5)
dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0],
    x, dout)

import numpy as np
from gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from layers import *

# Test the affine_backward function

x = np.random.randn(10, 2, 3)
w = np.random.randn(6, 5)
b = np.random.randn(5)
dout = np.random.randn(10, 5)

dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)
