NumPyCNN

Convolutional neural network implementation using NumPy. Just three layers are created which are convolution (conv for short), ReLU, and max pooling. The major steps involved are as follows:

1. Reading the input image.
2. Preparing filters.
3. Conv layer: Convolving each filter with the input image.
4. ReLU layer: Applying ReLU activation function on the feature maps (output of conv layer).
5. Max Pooling layer: Applying the pooling operation on the output of ReLU layer.
6. Stacking conv, ReLU, and max pooling layers

# Reading the image
#img = skimage.io.imread("fruits2.png")
img = skimage.data.chelsea()
# Converting the image into gray.
img = skimage.color.rgb2gray(img)

# First conv layer
#l1_filter = numpy.random.rand(2,7,7)*20 # Preparing the filters randomly.
l1_filter = numpy.zeros((2,3,3))
l1_filter[0, :, :] = numpy.array([[[-1, 0, 1], 
                                   [-1, 0, 1], 
                                   [-1, 0, 1]]])
l1_filter[1, :, :] = numpy.array([[[1,   1,  1], 
                                   [0,   0,  0], 
                                   [-1, -1, -1]]])

print("\n**Working with conv layer 1**")
l1_feature_map = conv(img, l1_filter)
print("\n**ReLU**")
l1_feature_map_relu = relu(l1_feature_map)
print("\n**Pooling**")
l1_feature_map_relu_pool = pooling(l1_feature_map_relu, 2, 2)
print("**End of conv layer 1**\n")

Here is the outputs of such conv-relu-pool layers.

# Second conv layer
l2_filter = numpy.random.rand(3, 5, 5, l1_feature_map_relu_pool.shape[-1])
print("\n**Working with conv layer 2**")
l2_feature_map = conv(l1_feature_map_relu_pool, l2_filter)
print("\n**ReLU**")
l2_feature_map_relu = relu(l2_feature_map)
print("\n**Pooling**")
l2_feature_map_relu_pool = pooling(l2_feature_map_relu, 2, 2)
print("**End of conv layer 2**\n")

The outputs of such conv-relu-pool layers are shown below.

# Third conv layer
l3_filter = numpy.random.rand(1, 7, 7, l2_feature_map_relu_pool.shape[-1])
print("\n**Working with conv layer 3**")
l3_feature_map = conv(l2_feature_map_relu_pool, l3_filter)
print("\n**ReLU**")
l3_feature_map_relu = relu(l3_feature_map)
print("\n**Pooling**")
l3_feature_map_relu_pool = pooling(l3_feature_map_relu, 2, 2)
print("**End of conv layer 3**\n")

The following graph shows the outputs of the above conv-relu-pool layers.

For more info.: KDnuggets: https://www.kdnuggets.com/author/ahmed-gad LinkedIn: https://www.linkedin.com/in/ahmedfgad Facebook: https://www.facebook.com/ahmed.f.gadd [email protected] [email protected]