如何将自己的数据集提供给 keras image_ocr

Question

我知道 keras image_ocr 模型。它使用图像生成器来生成图像，但是，我遇到了一些困难，因为我正在尝试将自己的数据集提供给模型以供 training.vi

存储库链接为：https://github.com/fchollet/keras/blob/master/examples/image_ocr.py

我创建了数组：x 和 y。我的图像路径及其相应的 gt 位于 csv 文件中。

x 的图像尺寸为： [nb_samples， w， h， c]

y 被赋予标签，这是一个字符串，即 gt。

这是我用来预处理的代码：

for i in range(0,len(read_file)):
path = read_file['path'][i]
label = read_file['gt'][i]
path = path.strip('n')
img = cv2.imread(path,0)
#Re-sizing the images
#height = 64, width = 128
#res_img = cv2.resize(img, (128,64))
#cv2.imwrite(i,res_img)
h,w =  img.shape
x.append(img)
y.append(label)
size = img.size
"""
print "Height: ", h #Height
print "Width: ", w #Width
print "Channel: ", c #Channel
print "Size: ", size
print "n"
"""
print "H: ", h
print "W: ", w
print "S: ", size
x = np.array(x).astype(np.float32)
y = np.array(y)
x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.3,random_state=42)
x_train = np.array(x_train).astype(np.float32)
y_train = np.array(y_train)
x_train = np.array(x_train)
x_test = np.array(x_test)
y_test = np.array(y_test)
print "Printing the shapes. n"
print "X_train shape: ", x_train.shape
print "Y_train shape: ", y_train.shape
print "X_test shape: ", x_test.shape
print "Y_test shape: ", y_test.shape
print "n"

后面跟着 keras image_ocr代码。总代码在这里： https://gist.github.com/kjanjua26/b46388bbde9ded5cf1f077a9f0dedc4f

我运行这个时的错误是：

`Traceback (most recent call last):
File "preprocess.py", line 323, in <module>
train(run_name, 0, 20, w)
File "preprocess.py", line 314, in train
model.fit(next_train(x_train), y_train, batch_size=7, epochs=20,       verbose=1, validation_split=0.1, shuffle=True, initial_epoch=0)
File "/home/kamranjanjua/anaconda2/lib/python2.7/site-  packages/keras/engine/training.py", line 1358, in fit
batch_size=batch_size)
File "/home/kamranjanjua/anaconda2/lib/python2.7/site-packages/keras/engine/training.py", line 1234, in _standardize_user_data
exception_prefix='input')
File "/home/kamranjanjua/anaconda2/lib/python2.7/site-packages/keras/engine/training.py", line 100, in _standardize_input_data
'Found: ' + str(data)[:200] + '...')
TypeError: Error when checking model input: data should be a Numpy array, or list/dict of Numpy arrays. Found: <generator object next_train at 0x7f8752671640>...`

任何帮助将不胜感激。

Answer 1

如果您仔细查看代码，您将能够看到模型需要字典作为其输入。

inputs = {'the_input': X_data,'the_labels': labels, 'input_length': input_length,'label_length': label_length,'source_str': source_str}
outputs = {'ctc': np.zeros([size])}  # dummy data for dummy loss function

对于输入： 1( X_data是训练示例 2( 标签是相应训练示例的标签 3( label_length是标签的长度 4( Input_Length是输入的长度 5(源字符串不是强制性的，只是用于解码

输出是 CTC 损失函数的虚拟数据

现在，在您的代码中，您只生成了X_train，y_train，但缺少其他输入。您需要根据模型的预期输入和输出准备数据集。