def create_network(window, input):
network_first = create_regressor(batchsize=batchsize,
window=window,
input=input,
dropout=0.0)
network_second = create_core(batchsize=batchsize,
window=window,
dropout=0.0,
depooler=lambda x, **kw: x / 2)
network_second.load(np.load('network_core.npz'))
network = Network(network_first,
network_second[1],
params=network_first.params)
network.load(np.load('network_regression.npz'))
return network_first, network_second, network
feet = np.array([9, 10, 11, 12, 13, 14, 21, 22, 23, 24, 25, 26])
Y = X[:, feet]
I = np.arange(len(X))
rng.shuffle(I)
X, Y = X[I], Y[I]
batchsize = 1
window = X.shape[2]
network_second = create_core(batchsize=batchsize,
window=window,
dropout=0.0,
depooler=lambda x, **kw: x / 2)
network_second.load(np.load('network_core.npz'))
network_first = create_regressor(batchsize=batchsize,
window=window,
input=Y.shape[1])
network = Network(network_first,
network_second[1],
params=network_first.params)
E = theano.shared(X, borrow=True)
F = theano.shared(Y, borrow=True)
trainer = AdamTrainer(rng=rng, batchsize=batchsize, epochs=100, alpha=0.00001)
trainer.train(network, F, E, filename='network_regression_kick.npz')
feet = np.array([27, 28, 29])
Y = X[:, feet]
print('X:', X)
print('Y: ', Y)
#Y shape: 121,12,240
#m.getch()
frames = np.hstack(np.arange(116, 125))
test = Y[:, :, frames]
test_gt = X[:, :, frames]
print('test:', test.shape)
batchsize = 1
window = test.shape[2]
network_first = create_regressor(batchsize=batchsize,
window=window,
input=test.shape[1],
dropout=0.0)
network_second = create_core(batchsize=batchsize,
window=window,
dropout=0.0,
depooler=lambda x, **kw: x / 2)
#print('network_second[1]: ',network_second[0])
#print('network_second params: ',network_second.params)
#network_second.params=[W,b,W,b]
#print('after network_second')
network_second.load(np.load('network_core.npz'))
#print('after network_second load')
#print('network_second params: ',network_second.params)
#network_second.params=[W,b,W,b]
network = Network(network_first,
rng.shuffle(I)
data_train = data[I[:len(data) // 2]]
data_valid = data[I[len(data) // 2:]]
X = data_valid
X = np.swapaxes(X, 1, 2).astype(theano.config.floatX)
preprocess = np.load('preprocess_core.npz')
X = (X - preprocess['Xmean']) / preprocess['Xstd']
Y = X[:, -7:]
batchsize = 1
network_first = create_regressor(batchsize=batchsize,
window=X.shape[2],
input=Y.shape[1],
dropout=0.0)
network_second = create_core(batchsize=batchsize,
window=X.shape[2],
dropout=0.0,
depooler=lambda x, **kw: x / 2)
network = Network(network_first,
network_second[1],
params=network_first.params)
network.load(np.load('network_regression.npz'))
from AnimationPlot import animation_plot
for i in range(5):
index = rng.randint(len(X) - 1)
