def __init__(self) -> None:
size = img_size
self.conv1 = convolution.Convolution(input_shape=(size, size, ci),
out_channel=oc1,
kernel_size=(ks1, ks1),
stride=(stride1, stride1),
learning_rate=conv1_learning_rate,
activate_func='relu')
assert (size - ks1) % stride1 == 0
size = (size - ks1) // stride1 + 1
self.pool1 = pooling.Pooling(input_shape=(size, size, oc1),
pool_size=ps1)
assert size % 2 == 0
size //= 2
self.conv2 = convolution.Convolution(input_shape=(size, size, oc1),
out_channel=oc2,
kernel_size=(ks2, ks2),
stride=(stride2, stride2),
learning_rate=conv2_learning_rate,
activate_func='relu')
assert (size - ks2) % stride2 == 0
size = (size - ks2) // stride2 + 1
self.pool2 = pooling.Pooling(input_shape=(size, size, oc2),
pool_size=ps2)
assert size % 2 == 0
size //= 2
self.conv3 = convolution.Convolution(input_shape=(size, size, oc2),
out_channel=oc3,
kernel_size=(ks3, ks3),
stride=(stride3, stride3),
learning_rate=conv3_learning_rate,
activate_func='relu')
assert (size - ks3) % stride3 == 0
size = (size - ks3) // stride3 + 1
self.pool3 = pooling.Pooling(input_shape=(size, size, oc3),
pool_size=ps3)
assert size % 2 == 0
size //= 2
self.conv4 = convolution.Convolution(input_shape=(size, size, oc3),
out_channel=oc4,
kernel_size=(ks4, ks4),
stride=(stride4, stride4),
learning_rate=conv4_learning_rate,
activate_func='relu')
assert (size - ks4) % stride4 == 0
size4 = (size - ks4) // stride4 + 1
self.fc1 = full_connection.FullConnection(
dim_in=size * size * oc3,
dim_out=deepid_dim // 2,
learning_rate=fc1_learning_rate,
activate_func='relu')
self.fc2 = full_connection.FullConnection(
dim_in=size4 * size4 * oc4,
dim_out=deepid_dim // 2,
learning_rate=fc2_learning_rate,
activate_func='relu')
for i in range(0, len(test_lables)):
seq_id = processing.seq2id(test_sentences[i], word2id)
target_id = test_lables[i]
test_x.append(seq_id)
test_y.append(int(target_id))
dev_x_tensor = data_loader.pad(dev_x, word2id, "-pad-")
dev_y_tensor = torch.from_numpy(np.array(dev_y))
test_x_tensor = data_loader.pad(test_x, word2id, "-pad-")
test_y_tensor = torch.from_numpy(np.array(test_y))
# print(test_x_tensor)
plot_dev = []
plot_test = []
x = []
model = pooling.Pooling(len(word2id), 100, 2)
with torch.no_grad():
for i in range(100):
# model = pooling.Pooling(len(word2id), 100, 2)
model.eval()
model.load_state_dict(torch.load(model_url + "/" + str(i + 1) + ".pt"))
correct = 0
total = 0
outputs1 = model(dev_x_tensor.long())
dev_right_num = train.accuracy_num(outputs1, dev_y_tensor)
dev_acc = float(dev_right_num) / len(dev_y_tensor) * 100
outputs2 = model(test_x_tensor.long())
test_right_num = train.accuracy_num(outputs2, test_y_tensor)
test_acc = float(test_right_num) / len(test_y_tensor) * 100
plot_dev.append(dev_acc)
def __init__(self) -> None:
self.conv1 = convolution.Convolution((130, 130, 3),
out_channel=96,
kernel_size=(5, 5),
stride=(1, 1),
learning_rate=learning_rate,
padding=(1, 1),
activate_func='mfm')
self.conv2a = convolution.Convolution((64, 64, 48),
out_channel=96,
kernel_size=(1, 1),
stride=(1, 1),
learning_rate=learning_rate,
activate_func='mfm')
self.conv2 = convolution.Convolution((64, 64, 48),
out_channel=192,
kernel_size=(3, 3),
stride=(1, 1),
learning_rate=learning_rate,
padding=(1, 1),
activate_func='mfm')
self.conv3a = convolution.Convolution((32, 32, 96),
out_channel=192,
kernel_size=(1, 1),
stride=(1, 1),
learning_rate=learning_rate,
activate_func='mfm')
self.conv3 = convolution.Convolution((32, 32, 96),
out_channel=384,
kernel_size=(3, 3),
stride=(1, 1),
learning_rate=learning_rate,
padding=(1, 1),
activate_func='mfm')
self.conv4a = convolution.Convolution((16, 16, 192),
out_channel=384,
kernel_size=(1, 1),
stride=(1, 1),
learning_rate=learning_rate,
activate_func='mfm')
self.conv4 = convolution.Convolution((16, 16, 192),
out_channel=256,
kernel_size=(3, 3),
stride=(1, 1),
learning_rate=learning_rate,
padding=(1, 1),
activate_func='mfm')
self.conv5a = convolution.Convolution((16, 16, 128),
out_channel=256,
kernel_size=(1, 1),
stride=(1, 1),
learning_rate=learning_rate,
activate_func='mfm')
self.conv5 = convolution.Convolution((16, 16, 128),
out_channel=256,
kernel_size=(3, 3),
stride=(1, 1),
learning_rate=learning_rate,
padding=(1, 1),
activate_func='mfm')
self.pool1 = pooling.Pooling((128, 128, 48), pool_size=2)
self.pool2 = pooling.Pooling((64, 64, 96), pool_size=2)
self.pool3 = pooling.Pooling((32, 32, 192), pool_size=2)
self.pool4 = pooling.Pooling((16, 16, 128), pool_size=2)
self.fc1 = full_connection.FullConnection(8 * 8 * 128,
512,
learning_rate=learning_rate,
activate_func='mfm')
self.fc_out = full_connection.FullConnection(
256, class_num, learning_rate=learning_rate)