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GitHub - csanycall/NumPyCNN: Convolutional neural network implementation using NumPy

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. l1

# 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. l2

# 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. l3

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 ahmed.f.gad@gmail.com ahmed.fawzy@ci.menofia.edu.eg