def body2(i, x, out):
# Convolution Layer
inputx = x.read(index=i)
conv1 = func.conv2d(inputx, weights['wc1'], biases['bc1'])
# Pooling (down-sampling)
p1 = func.extract_patches(conv1, 'SAME', 2, 2)
f1 = func.majority_frequency(p1)
# maxpooling
pool1 = func.majority_pool(p=p1, f=f1)
# Convolution Layer
conv2 = func.conv2d(pool1, weights['wc2'], biases['bc2'])
# Pooling (down-sampling)
p2 = func.extract_patches(conv2, 'SAME', 2, 2)
f2 = func.majority_frequency(p2)
# maxpooling
pool2 = func.majority_pool(p=p2, f=f2)
# Fully connected layer
# Reshape conv2 output to fit fully connected layer input
fc = tf.reshape(pool2, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
# Apply Dropout
fc1 = tf.nn.dropout(fc1, dropout)
# Output, class prediction
out = out.write(index=i,
value=tf.add(tf.matmul(fc1, weights['out']),
biases['out']))
i += 1
return i, x, out
def body(i, x, y, grads):
# Convolution Layer
inputx = x.read(index=i)
conv1 = func.conv2d(inputx, weights['wc1'], biases['bc1'])
# Pooling (down-sampling)
p1 = func.extract_patches(conv1, 'SAME', 2, 2)
f1 = func.majority_frequency(p1)
# maxpooling
pool1, mask1 = func.pca_pool_with_mask(temp=p1)
# Convolution Layer
conv2 = func.conv2d(pool1, weights['wc2'], biases['bc2'])
# Pooling (down-sampling)
p2 = func.extract_patches(conv2, 'SAME', 2, 2)
f2 = func.majority_frequency(p2)
# maxpooling
pool2, mask2 = func.pca_pool_with_mask(temp=p2)
# Fully connected layer
# Reshape conv2 output to fit fully connected layer input
fc = tf.reshape(pool2, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
# Apply Dropout
fc1 = tf.nn.dropout(fc1, dropout)
# Output, class prediction
yi = y.read(index=i)
temp_pred = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
grads[8] = pred.write(index=i, value=temp_pred)
# ------------------------------end of define graph------------------------------------
# ------------------------------define gradient descent-------------------------
# the last fc
e = tf.nn.softmax(temp_pred) - yi
grads[3] = grads[3].write(index=i, value=tf.transpose(fc1) @ e)
grads[7] = grads[7].write(index=i, value=tf.reduce_sum(e, axis=0))
# the second last fc
# we use droupout at the last second layer, then we should just update the nodes that are active
e = tf.multiply(e @ tf.transpose(weights['out']), tf.cast(tf.greater(fc1, 0), dtype=tf.float32)) / dropout
grads[2] = grads[2].write(index=i, value=tf.transpose(fc) @ e)
grads[6] = grads[6].write(index=i, value=tf.reduce_sum(e, axis=0))
# the last pooling layer
e = e @ tf.transpose(weights['wd1'])
e = tf.reshape(e, pool2.get_shape().as_list())
# the last conv layer
# unpooling get error from pooling layer
e = func.error_pooling2conv(e, mask2)
# multiply with the derivative of the active function on the conv layer
# this one is also important this is a part from the upsampling, but
e = tf.multiply(e, tf.cast(tf.greater(conv2, 0), dtype=tf.float32))
temp1, temp2 = func.filter_gradient(e, pool1, conv2)
grads[1] = grads[1].write(index=i, value=temp1)
grads[5] = grads[5].write(index=i, value=temp2)
# conv to pool
e = func.error_conv2pooling(e, weights['wc2'])
# pool to the first conv
e = func.error_pooling2conv(e, mask1)
e = tf.multiply(e, tf.cast(tf.greater(conv1, 0), dtype=tf.float32))
temp1, temp2 = func.filter_gradient(e, inputx, conv1)
grads[0] = grads[0].write(index=i, value=temp1)
grads[4] = grads[4].write(index=i, value=temp2)
i += 1
return i, x, y, grads