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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:
- Reading the input image.
- Preparing filters.
- Conv layer: Convolving each filter with the input image.
- ReLU layer: Applying ReLU activation function on the feature maps (output of conv layer).
- Max Pooling layer: Applying the pooling operation on the output of ReLU layer.
- 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 ahmed.f.gad@gmail.com ahmed.fawzy@ci.menofia.edu.eg