dbn.train_greedylayerwise(vis_trainset=train_imgs,
lbl_trainset=train_lbls,
n_iterations=10)
# dbn.recognize(train_imgs, train_lbls)
#for digit in range(1,10):
# digit_1hot = np.zeros(shape=(1, 10))
# digit_1hot[0, digit] = 1
# print (digit_1hot)
# dbn.generate(digit_1hot, name="rbms")
""" fine-tune wake-sleep training """
dbn: DeepBeliefNet = load("savefiles/dbn_greedy.pkl")
dbn.recognize(test_imgs[:1000], test_lbls[:1000])
dbn.train_wakesleep_finetune(vis_trainset=train_imgs,
lbl_trainset=train_lbls,
n_iterations=10)
from simple_dbn import save, load
save(dbn, "savefiles/originaldbn_finetuned")
dbn.recognize(test_imgs[:1000], test_lbls[:1000])
# acc = np.load("trained_dbn/accuracy_reco_finetune_normal_dbn.npy")
# plt.plot(range(len(acc)), acc)
# plt.xlabel("Fine Tuning Epochs")
image_size = [28,28]
train_imgs,train_lbls,test_imgs,test_lbls = read_mnist(dim=image_size, n_train=60000, n_test=10000)
''' deep- belief net '''
print ("\nStarting a Deep Belief Net..")
batch_size = 20
dbn = DeepBeliefNet(sizes={"vis":image_size[0]*image_size[1], "hid":500, "pen":500, "top":2000, "lbl":10},
image_size=image_size,
n_labels=10,
batch_size=batch_size
)
''' greedy layer-wise training '''
epochs = 10
iterations = int(60000*epochs/batch_size)
dbn.train_greedylayerwise(vis_trainset=train_imgs, lbl_trainset=train_lbls, n_iterations=iterations)
#dbn.recognize(train_imgs, train_lbls)
dbn.recognize(test_imgs, test_lbls)
for digit in range(10):
digit_1hot = np.zeros(shape=(1,10))
digit_1hot[0,digit] = 1
dbn.generate(digit_1hot, name="rbms")
print ("\nStarting a Deep Belief Net..")
dbn = DeepBeliefNet(sizes={"vis":image_size[0]*image_size[1], "hid":500, "pen":500, "top":2000, "lbl":10},
image_size=image_size,
n_labels=10,
batch_size=10
)
''' greedy layer-wise training '''
dbn.train_greedylayerwise(vis_trainset=train_imgs, lbl_trainset=train_lbls, n_iterations=1)
dbn.recognize(train_imgs, train_lbls)
dbn.recognize(test_imgs, test_lbls)
#%%
dbn = DeepBeliefNet(sizes={"vis":image_size[0]*image_size[1], "hid":500, "pen":500, "top":2000, "lbl":10},
image_size=image_size,
n_labels=10,
batch_size=10)
dbn.train_greedylayerwise(vis_trainset=train_imgs, lbl_trainset=train_lbls, n_iterations=1)
#dbn.recognize(train_imgs, train_lbls)
#dbn.recognize(test_imgs, test_lbls)
"vis": image_size[0] * image_size[1],
"hid": 500,
"pen": 500,
"top": 2000,
"lbl": 10
},
image_size=image_size,
n_labels=10,
batch_size=10)
''' greedy layer-wise training '''
dbn.train_greedylayerwise(vis_trainset=train_imgs,
lbl_trainset=train_lbls,
n_iterations=10000)
dbn.recognize(train_imgs, train_lbls)
dbn.recognize(test_imgs, test_lbls)
for digit in range(10):
digit_1hot = np.zeros(shape=(1, 10))
digit_1hot[0, digit] = 1
dbn.generate(digit_1hot, name="rbms")
''' fine-tune wake-sleep training '''
dbn.train_wakesleep_finetune(vis_trainset=train_imgs,
lbl_trainset=train_lbls,
n_iterations=10000)
dbn.recognize(train_imgs, train_lbls)
"vis": image_size[0] * image_size[1],
"hid": 500,
"pen": 500,
"top": 2000,
"lbl": 10
},
image_size=image_size,
n_labels=10,
batch_size=10)
''' greedy layer-wise training '''
dbn.train_greedylayerwise(vis_trainset=train_imgs,
lbl_trainset=train_lbls,
n_iterations=10000)
print("Done with greedy layer-wise")
dbn.recognize(train_imgs, train_lbls)
print("done with train recognize")
dbn.recognize(test_imgs, test_lbls)
print("done with test recognize")
print("Generate")
for digit in range(10):
digit_1hot = np.zeros(shape=(1, 10))
digit_1hot[0, digit] = 1
dbn.generate(digit_1hot, name="rbms")
''' fine-tune wake-sleep training '''
dbn.train_wakesleep_finetune(vis_trainset=train_imgs,
lbl_trainset=train_lbls,
n_iterations=2000)
print("Done with wakesleep")