我尝试训练图像并预测其中的文本。但是当一起训练图像时，我遇到了阵列错误。但是现在我正在训练每个字母的图像，但是却出现了一些错误。将添加图像生成器文件，以帮助创建图像并将其导入到拟合生成器。
错误：
Using TensorFlow backend.
WARNING: Logging before flag parsing goes to stderr.
W0826 09:18:45.040408 3428 deprecation_wrapper.py:119] From C:\Users\workspace\test\venv\lib\site-packages\keras\backend\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
W0826 09:18:45.056031 3428 deprecation_wrapper.py:119] From C:\Users\workspace\test\venv\lib\site-packages\keras\backend\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
W0826 09:18:45.071652 3428 deprecation_wrapper.py:119] From C:\Users\workspace\test\venv\lib\site-packages\keras\backend\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.
Traceback (most recent call last):
File "C:/Users/workspace/test/killme.py", line 22, in <module>
o2 = Reshape((len(string.ascii_uppercase), ), name="symbol_{}".format(i+1))(o)
File "C:\Users\workspace\test\venv\lib\site-packages\keras\engine\base_layer.py", line 474, in __call__
output_shape = self.compute_output_shape(input_shape)
File "C:\Users\workspace\test\venv\lib\site-packages\keras\layers\core.py", line 398, in compute_output_shape
input_shape[1:], self.target_shape)
File "C:\Users\workspace\test\venv\lib\site-packages\keras\layers\core.py", line 386, in _fix_unknown_dimension
raise ValueError(msg)
ValueError: total size of new array must be unchanged
代码：
from keras.models import Sequential, Input, Model
from keras.layers import Dense, Reshape
from keras.utils import to_categorical
from keras.layers.convolutional import Conv2D # to add convolutional layers
from keras.layers.convolutional import MaxPooling2D # to add pooling layers
from keras.layers import Flatten # to flatten data for fully connected layers
import string
from generators import ImageGenerator, BasicGenerator
from numpy import reshape
height=20
width=200
font_size=20
i1=Input(shape=(height, width, 1))
character_count=int(width / font_size)
outputs=[]
for i in range(character_count):
o = Dense(len(string.ascii_uppercase), activation='relu')(i1)
o2 = Reshape((len(string.ascii_uppercase), ), name="symbol_{}".format(i+1))(o)
outputs.append(o2)
string_model = Model(inputs=i1, outputs=outputs)
string_model.layers[2].layer.trainable = False
generator = ImageGenerator(height, width, font_size, character_count)
string_model.compile(loss="categorical_crossentropy", optimizer="rmsprop", metrics=["categorical_accuracy"])
string_model.summary()
string_model.fit_generator(generator,epochs=10)
答案 0 :(得分：0)
您必须对文本数据进行预处理，然后才将其密集地放入输出中。转换为vocab是一个更好的主意。创建一个CaptionGenerator使其变得简单，如下所示。
from vgg16 import VGG16
from keras.applications import inception_v3
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import LSTM, Embedding, TimeDistributed, Dense, RepeatVector, Merge, Activation, Flatten
from keras.preprocessing import image, sequence
from keras.callbacks import ModelCheckpoint
import cPickle as pickle
EMBEDDING_DIM = 128
class CaptionGenerator():
def __init__(self):
self.max_cap_len = None
self.vocab_size = None
self.index_word = None
self.word_index = None
self.total_samples = None
self.encoded_images = pickle.load( open( "encoded_images.p", "rb" ) )
self.variable_initializer()
def variable_initializer(self):
df = pd.read_csv('Flickr8k_text/flickr_8k_train_dataset.txt', delimiter='\t')
nb_samples = df.shape[0]
iter = df.iterrows()
caps = []
for i in range(nb_samples):
x = iter.next()
caps.append(x[1][1])
self.total_samples=0
for text in caps:
self.total_samples+=len(text.split())-1
print "Total samples : "+str(self.total_samples)
words = [txt.split() for txt in caps]
unique = []
for word in words:
unique.extend(word)
unique = list(set(unique))
self.vocab_size = len(unique)
self.word_index = {}
self.index_word = {}
for i, word in enumerate(unique):
self.word_index[word]=i
self.index_word[i]=word
max_len = 0
for caption in caps:
if(len(caption.split()) > max_len):
max_len = len(caption.split())
self.max_cap_len = max_len
print "Vocabulary size: "+str(self.vocab_size)
print "Maximum caption length: "+str(self.max_cap_len)
print "Variables initialization done!"
def data_generator(self, batch_size = 32):
partial_caps = []
next_words = []
images = []
print "Generating data..."
gen_count = 0
df = pd.read_csv('Flickr8k_text/flickr_8k_train_dataset.txt', delimiter='\t')
nb_samples = df.shape[0]
iter = df.iterrows()
caps = []
imgs = []
for i in range(nb_samples):
x = iter.next()
caps.append(x[1][1])
imgs.append(x[1][0])
total_count = 0
while 1:
image_counter = -1
for text in caps:
image_counter+=1
current_image = self.encoded_images[imgs[image_counter]]
for i in range(len(text.split())-1):
total_count+=1
partial = [self.word_index[txt] for txt in text.split()[:i+1]]
partial_caps.append(partial)
next = np.zeros(self.vocab_size)
next[self.word_index[text.split()[i+1]]] = 1
next_words.append(next)
images.append(current_image)
if total_count>=batch_size:
next_words = np.asarray(next_words)
images = np.asarray(images)
partial_caps = sequence.pad_sequences(partial_caps, maxlen=self.max_cap_len, padding='post')
total_count = 0
gen_count+=1
print "yielding count: "+str(gen_count)
yield [[images, partial_caps], next_words]
partial_caps = []
next_words = []
images = []
def load_image(self, path):
img = image.load_img(path, target_size=(224,224))
x = image.img_to_array(img)
return np.asarray(x)
def create_model(self, ret_model = False):
#base_model = VGG16(weights='imagenet', include_top=False, input_shape = (224, 224, 3))
#base_model.trainable=False
image_model = Sequential()
#image_model.add(base_model)
#image_model.add(Flatten())
image_model.add(Dense(EMBEDDING_DIM, input_dim = 4096, activation='relu'))
image_model.add(RepeatVector(self.max_cap_len))
lang_model = Sequential()
lang_model.add(Embedding(self.vocab_size, 256, input_length=self.max_cap_len))
lang_model.add(LSTM(256,return_sequences=True))
lang_model.add(TimeDistributed(Dense(EMBEDDING_DIM)))
model = Sequential()
model.add(Merge([image_model, lang_model], mode='concat'))
model.add(LSTM(1000,return_sequences=False))
model.add(Dense(self.vocab_size))
model.add(Activation('softmax'))
print "Model created!"
if(ret_model==True):
return model
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
return model
def get_word(self,index):
return self.index_word[index]
关注链接以获取更多信息：：https://github.com/arjun-kava/caption_generator