def generate_intermediate_layers(net):
"""Takes the output from the decapitated googlenet and transforms the output
from a NxCxWxH to (NxWxH)xCx1x1 that is used as input for the lstm layers.
N = batch size, C = channels, W = grid width, H = grid height."""
net.f(Convolution("post_fc7_conv", bottoms=["inception_final_output"],
param_lr_mults=[1., 2.], param_decay_mults=[0., 0.],
num_output=1024, kernel_dim=(1, 1),
weight_filler=Filler("gaussian", 0.005),
bias_filler=Filler("constant", 0.)))
net.f(Power("lstm_fc7_conv", scale=0.01, bottoms=["post_fc7_conv"]))
net.f(Transpose("lstm_input", bottoms=["lstm_fc7_conv"]))
import apollocaffe
from apollocaffe.layers import NumpyData, Convolution, EuclideanLoss
import numpy as np
net = apollocaffe.ApolloNet()
for i in range(1000):
example = np.array(np.random.random()).reshape((1, 1, 1, 1))
net.clear_forward()
net.f(NumpyData('data', example))
net.f(NumpyData('label', example * 3))
net.f(Convolution('conv', (1, 1), 1, bottoms=['data']))
net.f(EuclideanLoss('loss', bottoms=['conv', 'label']))
net.backward()
net.update(lr=0.1)
if i % 100 == 0:
print net.loss