x_train.shape[0]
x_train.shape[1]
x_train.shape[2]
layer = []
inp = Input(shape=(x_train.shape[1],))
print(inp)
inputs=[]
for i in range(x_train.shape[1]):
x = layers.Dense(units = 64, activation = "sigmoid")(inp)
x = layers.Dense(units = 64, activation = "sigmoid")(x)
x = layers.Dense(units = 64, activation = "sigmoid")(x)
layer.append(x)
inputs.append(inp)
print(inputs)
len(inputs)
x.shape
# x와 같은 크기의 텐서를 만들어준다
x = tf.zeros_like(x)
x.shape
# layer 리스트에 Dense들이 하나씩 들어가있다
print(layer)
layer
for k in layer:
for i, char in enumerate(ff):
char = char.strip()
char_list.append(char)
char2id = {j: i for i, j in enumerate(char_list)}
id2char = {i: j for i, j in enumerate(char_list)}
with open(label_txt_path, 'r', encoding='UTF-8') as f:
ff = f.readlines()
for i, line in enumerate(ff):
line = line.strip()
img_name = line.split(' ')[0]
label = line.split()[1:]
label = list(map(int, label))
img_names.append(img_name)
labels.append(label)
# img_names = random.shuffle(img_names)
# labels = random.shuffle(labels)
assert len(img_names) == len(labels), "len(img_names) !=len(labels)"
length_data = len(img_names)
train_img_names = img_names[:int(0.9 * length_data)]
train_labels = labels[:int(0.9 * length_data)]
test_img_names = img_names[int(0.9 * length_data):]
test_labels = labels[int(0.9 * length_data):]
print('train_img_nums:', len(train_img_names))
print('train_label_nums:', len(train_labels))
