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- def evaluate_lenet5(learning_rate=0.1, n_epochs=200, dataset='mnist.pkl.gz', nkerns=[2,5], batch_size=50):
- evaluate_lenet5
- original_image = cv2.imread('dataset')
- rng = numpy.random.RandomState(23455)
- datasets = load_data(dataset)
- train_set_x, train_set_y = datasets[0]
- valid_set_x, valid_set_y = datasets[1]
- test_set_x, test_set_y = datasets[2]
- n_train_batches = train_set_x.get_value(borrow=True).shape[0]
- n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
- n_test_batches = test_set_x.get_value(borrow=True).shape[0]
- n_train_batches //= batch_size
- n_valid_batches //= batch_size
- n_test_batches //= batch_size
- index = T.lscalar()
- x = T.matrix('x')
- y = T.ivector('y')
- print('... building the model')
- layer0_input = x.reshape((batch_size, 1, 28, 28))
- layer0 = LeNetConvPoolLayer(
- rng,
- input=layer0_input,
- image_shape=(batch_size, 1, 28, 28),
- filter_shape=(nkerns[0], 1, 5, 5),
- poolsize=(2, 2)
- )
- layer1 = LeNetConvPoolLayer(
- rng,
- input=layer0.output,
- image_shape=(batch_size, nkerns[0], 12, 12),
- filter_shape=(nkerns[1], nkerns[0], 5, 5),
- poolsize=(2, 2)
- )
- layer00_input = x.reshape((batch_size, 1, 28, 28))
- layer00 = LeNetConvPoolLayer(
- rng,
- input=layer00_input,
- image_shape=(batch_size, 1, 28, 28),
- filter_shape=(nkerns[0], 1, 5, 5),
- poolsize=(2, 2)
- )
- layer11 = LeNetConvPoolLayer(
- rng,
- input=layer00.output,
- image_shape=(batch_size, nkerns[0], 12, 12),
- filter_shape=(nkerns[1], nkerns[0], 5, 5),
- poolsize=(2, 2)
- )
- pdb.set_trace()
- layer2_input1=layer1.output.flatten(2)
- layer2_input2=layer11.output.flatten(2)
- input1=numpy.asarray(layer2_input1)
- input2=numpy.asarray(layer2_input2)
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