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mw3i/cnn_classifier_in_autograd.py

Created June 28, 2019 00:42
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Convolutional Net in Autograd
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cnn_classifier_in_autograd.py
## std lib
import sys, os
## ext req
import autograd.numpy as np
from autograd import grad
import autograd.scipy.signal as signal
## _ _ _ Get Model Output _ _ _
def forward(model, inputs = None, hps = None):
conv_act = hps['c1_activation'](
np.add(
signal.convolve(
inputs,
model['input']['c1']['weights']
),
model['input']['c1']['bias']
)
).reshape(inputs.shape[0], -1) # <-- flatten final activations
dense1_act = hps['d1_activation'](
np.add(
np.matmul(
conv_act,
model['c1']['d1']['weights']
),
model['c1']['d1']['bias']
)
)
output_act = hps['output_activation'](
np.add(
np.matmul(
dense1_act,
model['d1']['output']['weights']
),
model['d1']['output']['bias']
)
)
return output_act
## _ _ _ Cost Function _ _ _
# cross entropy loss function
def loss(params, inputs = None, targets = None, hps = None):
model_output = forward(params, inputs = inputs, hps = hps)
return -np.sum(
targets * np.log(model_output),
)
loss_grad = grad(loss)
## _ _ _ Build Model _ _ _
def build_params(input_dimensions, categories, hps):
c1_size_flattened = np.prod([input_dimensions[-3], input_dimensions[-2]+2, input_dimensions[-1]+2, hps['c1_filters']])
return {
'input': {
'c1': {
'weights': np.random.uniform(*hps['wr'], [1, hps['c1_filters'], *hps['c1_filtersize']]), # <-- feature maps
'bias': np.zeros([1, hps['c1_filters'], 1, 1])
},
},
'c1': {
'd1': {
'weights': np.random.uniform(*hps['wr'], [c1_size_flattened, hps['d1_nodes']]),
'bias': np.zeros([1,hps['d1_nodes']]),
},
},
'd1': {
'output': {
'weights': np.random.uniform(*hps['wr'], [hps['d1_nodes'],len(categories)]),
'bias': np.zeros([1,len(categories)]),
},
},
}
def update_params(params, gradients, lr):
for layer in params:
for connection in params[layer]:
params[layer][connection]['weights'] -= lr * gradients[layer][connection]['weights']
params[layer][connection]['bias'] -= lr * gradients[layer][connection]['bias']
return params
if __name__ == '__main__':
## _ _ _ Load data _ _ _
## generate made up images
inputs = np.array([
## square small light
[[
[.0, .0, .0, .0, .0, .0, .0],
[.0, .0, .0, .0, .0, .0, .0],
[.0, .0, 1., 1., 1., .0, .0],
[.0, .0, 1., .2, 1., .0, .0],
[.0, .0, 1., 1., 1., .0, .0],
[.0, .0, .0, .0, .0, .0, .0],
[.0, .0, .0, .0, .0, .0, .0],
]],
## square large light
[[
[.0, .0, .0, .0, .0, .0, .0],
[.0, 1., 1., 1., 1., 1., .0],
[.0, 1., .2, .2, .2, 1., .0],
[.0, 1., .2, .2, .2, 1., .0],
[.0, 1., .2, .2, .2, 1., .0],
[.0, 1., 1., 1., 1., 1., .0],
[.0, .0, .0, .0, .0, .0, .0],
]],
## square small dark
[[
[.0, .0, .0, .0, .0, .0, .0],
[.0, .0, .0, .0, .0, .0, .0],
[.0, .0, 1., 1., 1., .0, .0],
[.0, .0, 1., .8, 1., .0, .0],
[.0, .0, 1., 1., 1., .0, .0],
[.0, .0, .0, .0, .0, .0, .0],
[.0, .0, .0, .0, .0, .0, .0],
]],
## square large dark
[[
[.0, .0, .0, .0, .0, .0, .0],
[.0, 1., 1., 1., 1., 1., .0],
[.0, 1., .8, .8, .8, 1., .0],
[.0, 1., .8, .8, .8, 1., .0],
[.0, 1., .8, .8, .8, 1., .0],
[.0, 1., 1., 1., 1., 1., .0],
[.0, .0, .0, .0, .0, .0, .0],
]],
## diamond small light
[[
[.0, .0, .0, .0, .0, .0, .0],
[.0, .0, .0, .0, .0, .0, .0],
[.0, .0, .0, 1., .0, .0, .0],
[.0, .0, 1., .2, 1., .0, .0],
[.0, .0, .0, 1., .0, .0, .0],
[.0, .0, .0, .0, .0, .0, .0],
[.0, .0, .0, .0, .0, .0, .0],
]],
## diamond large light
[[
[.0, .0, .0, .0, .0, .0, .0],
[.0, .0, .0, 1., .0, .0, .0],
[.0, .0, 1., .2, 1., .0, .0],
[.0, 1., .2, .2, .2, 1., .0],
[.0, .0, 1., .2, 1., .0, .0],
[.0, .0, .0, 1., .0, .0, .0],
[.0, .0, .0, .0, .0, .0, .0],
]],
## diamond small dark
[[
[.0, .0, .0, .0, .0, .0, .0],
[.0, .0, .0, .0, .0, .0, .0],
[.0, .0, .0, 1., .0, .0, .0],
[.0, .0, 1., .8, 1., .0, .0],
[.0, .0, .0, 1., .0, .0, .0],
[.0, .0, .0, .0, .0, .0, .0],
[.0, .0, .0, .0, .0, .0, .0],
]],
## diamond large dark
[[
[.0, .0, .0, .0, .0, .0, .0],
[.0, .0, .0, 1., .0, .0, .0],
[.0, .0, 1., .8, 1., .0, .0],
[.0, 1., .8, .8, .8, 1., .0],
[.0, .0, 1., .8, 1., .0, .0],
[.0, .0, .0, 1., .0, .0, .0],
[.0, .0, .0, .0, .0, .0, .0],
]],
])
labels = [
# 'A','A','A','A', 'B','B','B','B', # <-- type 1
# 'A','A','B','B', 'B','B','A','A', # <-- type 2
'A','A','A','B', 'B','B','B','A', # <-- type 4
# 'B','A','A','B', 'A','B','B','A', # <-- type 6
]
categories = np.unique(labels)
idx_map = {category: idx for category, idx in zip(categories, range(len(categories)))}
labels_indexed = [idx_map[label] for label in labels]
one_hot_targets = np.eye(len(categories))[labels_indexed]
## _ _ _ Define Hyperparameters _ _ _
hps = {
'lr': .1, # <-- learning rate
'wr': [-.1,.1], # <-- weight range
'c1_activation': lambda x: x * (x > 1), # relu
'd1_activation': lambda x: 1 / (1 + np.exp(-x)), # sigmoid
'output_activation': lambda x: np.exp(x) / np.sum(np.exp(x), axis = 1, keepdims = True), # softmax
'c1_filters': 6, # <-- conv layer
'c1_filtersize': [3,3], # <-- dimensions of filters (scipy will call you out if the size doesn't work)
'd1_nodes': 10, # <-- dense layer
}
## _ _ _ Build Model _ _ _
params = build_params(
inputs.shape, # <-- input dimensions
categories,
hps,
)
## _ _ _ Run MODEL _ _ _
print('- - - RUN MODEL - - -')
init_loss = loss(params, inputs = inputs, targets = one_hot_targets, hps = hps)
init_maps = params['input']['c1']['weights'].copy()
num_epochs = 1
for epoch in range(num_epochs):
gradients = loss_grad(params, inputs = inputs, targets = one_hot_targets, hps = hps)
params = update_params(params, gradients, hps['lr'])
print('- - - done - - - ')
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