我正在尝试基于keras ocr示例：链接实现手写OCR。但是我会收到以下错误：
InvalidArgumentError: All labels must be nonnegative integers, batch: 0 labels: 1,0,11,9,45,0,25,17,27,41,39,9,37,0,23,1,39,9,35,0,11,35,29,25,0,1,0,27,9,1,35,3,49,0,43,17,23,23,1,13,9,0,69,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1
[[{{node ctc_6/CTCLoss}}]]
[[{{node training_5/SGD/gradients/ctc_6/CTCLoss_grad/mul}}]]
这是发电机，CTC和火车功能：
def ctc_lambda_func(args):
y_pred, labels, input_length, label_length = args
# the 2 is critical here since the first couple outputs of the RNN
# tend to be garbage:
y_pred = y_pred[:, 2:, :]
return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
#Generation of data: load the images, resize, gray, normalize them
class DataGenerator(keras.utils.Sequence):
def __init__(self, list_Files, labels,downsample_factor, max_string_length=80, batch_size=32, dim=(512,64), shuffle=True):
self.dim = dim
self.batch_size = batch_size
self.labels = labels
self.list_Files = list_Files
self.shuffle = shuffle
self.on_epoch_end()
self.max_string_length = max_string_length
self.downsample_factor = downsample_factor
#TODO: Add weight save
def on_epoch_end(self):
self.indexes = np.arange(len(self.list_Files))
if self.shuffle==True:
np.random.shuffle(self.indexes)
def __data_generation(self, list_Files_temp):
#*[2,2] --> 2,2 (unpack values)
X = np.ones([self.batch_size, *self.dim,1])
y = np.ones([self.batch_size, self.max_string_length])*-1 #As in the keras_ocr example why -1?
X_length = np.zeros([self.batch_size,1])
y_length = np.zeros([self.batch_size,1])
#TODO: add mix with blank inputs as it is said to be important for transitional invariance
for i, file in enumerate(list_Files_temp):
im = cv2.imread(file)# load the file as numpy array
im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY) #Transform the file into a Gray image
im = cv2.resize(im, self.dim[::-1]) #Resize it (cv2 takes width first)
im = im / 255 #Normalization
X[i,0:self.dim[0],:,0] = im
X_length[i] = self.dim[0] // self.downsample_factor -2 #?????
seq = text_to_labels(self.labels[file])
y[i,0:len(seq)] = text_to_labels(self.labels[file]) #Transform the text into a list of integers
y_length[i] = len(y[i])
print("LEN={0}".format(y_length[i]))
inputs={'the_input': X,
'the_labels': y,
'input_length':X_length,
'label_length':y_length
}
outputs = {'ctc': np.zeros([self.batch_size])}
print(y)
return (inputs, outputs)
def __len__(self):
'Number of batches per epoch'
return int(np.floor(len(self.list_Files) / self.batch_size))
def __getitem__(self, index):
indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]
list_Files_temp = [self.list_Files[k] for k in indexes]
#print(list_Files_temp[0])
(inputs, outputs) = self.__data_generation(list_Files_temp)
return (inputs, outputs)
def train(dim_images,partition,labels):
#Misc parameters
absolute_max_string_length = 80
output_size = len(alphabet) + 1 #+1 for the CTC blank symbol
#Network parameters
img_h = dim_images[0]
img_w = dim_images[1]
conv_filters = 16
kernel_size = (3,3)
pool_size = 2
time_dense_size = 32
rnn_size = 512
act = 'relu'
input_shape = (*DIM_IMAGES,1)
downsample_factor = pool_size**2
#Convolutional layer
input_data = Input(name='the_input', shape=input_shape)
inner = Conv2D(conv_filters, kernel_size, padding='same',
activation=act, kernel_initializer='he_normal', name='conv1')(input_data)
inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max1')(inner)
inner = Conv2D(conv_filters, kernel_size, padding='same',
activation=act, kernel_initializer='he_normal',
name='conv2')(inner)
inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max2')(inner)
conv_to_rnn_dims = (img_w // (pool_size ** 2), (img_h // (pool_size ** 2)) * conv_filters)
inner = Reshape(target_shape=conv_to_rnn_dims, name='reshape')(inner)
#Recurrent layer
gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(inner)
gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(inner)
gru1_merged = add([gru_1, gru_1b])
gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)
gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)
# transforms RNN output to character activations:
inner = Dense(output_size, kernel_initializer='he_normal',
name='dense2')(concatenate([gru_2, gru_2b]))
#Prediction (need to be decoded)
y_pred = Activation('softmax', name='softmax')(inner)
Model(inputs=input_data, outputs=y_pred).summary()
labelsI = Input(name='the_labels',
shape =[absolute_max_string_length], dtype='float32')
input_length = Input(name='input_length', shape=[1], dtype='int64')
label_length = Input(name='label_length', shape=[1], dtype='int64')
# Keras doesn't currently support loss funcs with extra parameters
# so CTC loss is implemented in a lambda layer
loss_out = Lambda(
ctc_lambda_func, output_shape=(1,),
name='ctc')([y_pred, labelsI, input_length, label_length])
#Genrators
training_generator = DataGenerator(partition['train'],labels,downsample_factor, batch_size=BATCH_SIZE, dim=DIM_IMAGES, shuffle=True)
valid_generator = DataGenerator(partition['valid'], labels,downsample_factor, batch_size=BATCH_SIZE, dim=DIM_IMAGES, shuffle=False)
# clipnorm seems to speeds up convergence
sgd = SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)
model = Model(inputs=[input_data, labelsI, input_length, label_length],
outputs=loss_out)
# the loss calc occurs elsewhere, so use a dummy lambda func for the loss
model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd)
# captures output of softmax so we can decode the output during visualization
test_func = K.function([input_data], [y_pred])
model.fit_generator(
generator=training_generator,
steps_per_epoch=(len(partition['train'])-len(partition['valid'])) // BATCH_SIZE,
epochs=20,
validation_data=valid_generator,
validation_steps=len(partition['valid'])//BATCH_SIZE)
我猜'-1'标签来自此行：
y = np.ones([self.batch_size, self.max_string_length])*-1
在原始代码中，有类似的行(行220(，但运行良好：
self.Y_data = np.ones([self.num_words, self.absolute_max_string_len]) * -1
我认为'-1'是填充序列的一种方式，但是CTC函数似乎禁止此值，我在这里缺少某些东西吗？
看来我只是混合了图像长度和图像宽度。另外，" label_length"应等于句子的实际长度(在划为-1之前(。因此线：
y_length[i] = len(y[i])
应替换为：
y_length[i] = len(seq)