# split test and train data
v_train, v_test = np.split(v_all, [num_train])
tp_train, tp_test = np.split(tp_all, [num_train])
xA_train, xA_test = np.split(xA_all, [num_train])
batchsize = 100
n_epoch = 300
# In[ ]:
# create SdA
n_hiddens = (12**2 * num_frame, 6**2 * num_frame)
sda = StackedDenoisingAutoencoder(num_pxmovie, n_hiddens)
sda.train(v_all, n_epoch=n_epoch)
sda.save('history', n_hiddens, n_epoch, batchsize)
# sda.load('history/SdA_layer(576, 64)_epoch300.pkl')
# split test and train data
yA_each = sda.predict(v_all, bAllLayer=True)
yA_all = yA_each[-1]
# yA_hidden1_all = yA_each[0]
yA_train, yA_test = np.split(yA_all, [num_train])
# check output histgram
dummy = plt.hist(np.reshape(yA_all, (-1, 1)), 50)
# In[ ]:
## draw weight
# split test and train data
v_train, v_test = np.split(v_all, [num_train])
tp_train, tp_test = np.split(tp_all, [num_train])
xA_train, xA_test = np.split(xA_all, [num_train])
batchsize = 100
n_epoch = 300
# In[ ]:
# create SdA
n_hiddens = (12**2*num_frame, 6**2*num_frame)
sda = StackedDenoisingAutoencoder(num_pxmovie, n_hiddens)
sda.train(v_all, n_epoch=n_epoch)
sda.save('history', n_hiddens, n_epoch, batchsize)
# sda.load('history/SdA_layer(576, 64)_epoch300.pkl')
# split test and train data
yA_each = sda.predict(v_all, bAllLayer=True)
yA_all = yA_each[-1]
# yA_hidden1_all = yA_each[0]
yA_train, yA_test = np.split(yA_all, [num_train])
# check output histgram
dummy = plt.hist(np.reshape(yA_all, (-1, 1)), 50)
# In[ ]:
## draw weight
