tp_all = tp_all[indices]
# 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[ ]:
v_all = v_all[indices]
tp_all = tp_all[indices]
# 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[ ]:
x_train = np.empty(0, dtype=np.float32)
label_train = np.empty(0, dtype=np.int32)
for i_set in range(n_hold-1):
v_train = utils.vstack_(v_train, v_s[set_l[i_set]])
x_train = utils.vstack_(x_train, x_s[set_l[i_set]])
label_train = utils.vstack_(label_train, label_x_s[set_l[i_set]])
v_train = np.reshape(v_train, (num_train_movie, -1))
x_train = np.reshape(x_train, (num_train_movie, -1))
label_train = np.reshape(label_train, (num_train_dataset, -1))
v_test = np.reshape(v_s[i], (num_test_movie, -1))
x_test = np.reshape(x_s[i], (num_test_movie, -1))
label_test = label_x_s[i]
# create SdA
sda = StackedDenoisingAutoencoder(num_pxmovie, n_hiddens, n_epoch=n_epoch_SdA, use_cuda=use_cuda)
sda.train(v_train)
# split test and train data
y_train_each = sda.predict(v_train, bAllLayer=True)
y_test_each = sda.predict(v_test, bAllLayer=True)
list_layer = []
for j in range(num_layers):
y_train = y_train_each[j]
y_test = y_test_each[j]
# separate x&y into other and self
x_test_split = [np.empty(0,dtype=np.float32), np.empty(0,dtype=np.float32)]
y_test_split = [np.empty(0,dtype=np.float32), np.empty(0,dtype=np.float32)]
for i_test in range(int(num_test_movie)):