矩阵大小不相容: ln[0]：[1:43264]，ln[16,512]

Question

我想要建立一个模型来分类和预测用户嘴唇上的单词。使用副词，共有142657幅图像已被预先处理，使用单个说话人的视频数据集，但我在运行模型时遇到了这个错误，甚至没有通过下面的第一个时代--我的代码

import os
from silence_tensorflow import silence_tensorflow

silence_tensorflow()
import tensorflow as tf
from tensorflow.keras.layers import Dense, Activation, Dropout, Input, Conv2D, \
MaxPooling2D, Flatten, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
tf.autograph.set_verbosity(0)
tf.get_logger().setLevel('ERROR')


 class AdverbNet(object):
   def __init__(self):
       self.Model = Sequential()
       self.build()

   def build(self):
       self.Model.add(Input(name='the_input', shape=(224, 224, 1), batch_size=16,   dtype='float32'))
       self.Model.add(Conv2D(32, (3, 3), activation='sigmoid', name='convo2'))
       self.Model.add(MaxPooling2D(pool_size=(2, 2)))
       self.Model.add(Conv2D(32, (3, 3), activation='sigmoid', name='convo3'))
       self.Model.add(MaxPooling2D(pool_size=(2, 2)))
       self.Model.add(Conv2D(64, (3, 3), activation='relu', name='convo4'))
       self.Model.add(MaxPooling2D(pool_size=(2, 2)))
       self.Model.add(Flatten())

       self.Model.add(Dense(512))
       self.Model.add(Dropout(0.5))
       self.Model.add(BatchNormalization(scale=False))
       self.Model.add(Activation('relu'))
       self.Model.add(Dropout(0.5))
       self.Model.add(Dense(4, activation='softmax'))

   def summary(self):
       self.Model.summary()




if __name__ == "__main__":
   common_path = 'C:/Users/Loide/Desktop/Liphy/'
   C = AdverbNet()
   C.Model.compile(optimizer="Adam", loss='categorical_crossentropy', metrics=['accuracy'])
   C.Model.summary()

with tf.device('/device:GPU:0')  
   batch_size = 16
   epochs = 32
   train_dir = common_path + 'Images/Adverb/'
   test_dir = common_path + 'Images/Adverb/'
   checkpoint_path = common_path + 'SavedModels/Adverb/'

train_image_generator = ImageDataGenerator(rescale=1. / 255)  # Generator for  training data generate training anD test set
train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,
    directory=train_dir,
    shuffle=True,
    target_size=(224, 224),
    class_mode='categorical',
    color_mode='grayscale')
    

test_image_generator = ImageDataGenerator(rescale=1. / 255)  # Generator for  test data
test_data_gen = test_image_generator.flow_from_directory(batch_size=batch_size,
    directory=test_dir,
    shuffle=False,
    target_size=(224, 224),
    class_mode='categorical',
    color_mode='grayscale')

callback = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy',
                                                patience=10,
                                                restore_best_weights=True,
                                                baseline=0.45)
history = C.Model.fit(train_data_gen,
                          steps_per_epoch=8916,  # Number of images // Batch size
    epochs=epochs,
    verbose=1,
    validation_data=test_data_gen,
    validation_steps=187,
    callbacks=[callback])

C.Model.save(checkpoint_path, save_format='tf')

**我得到以下错误**

[Matrix size-incompatible: In[0]: [1,43264], In[1]: [16,512]
         [[{{node gradient_tape/sequential/dense/MatMul/MatMul_1}}]] [Op:__inference_train_function_1179]

​

​

Answer 1

似乎您有两个矩阵(一个1x43265和一个16x512)。你试图将它们相乘，但它的乘积在数学上没有定义。你需要一个矩阵是一个矩阵，另一个矩阵是( be )矩阵。这就是为什么你的程序不能运行。如果您的图像是测试数据集，请一步一步地按照说明执行。如果不是，您的预处理可能是不好的。

Answer 2

很容易，您需要将输出与损失函数和目标分类相匹配，请参阅我的更新示例。

样本：

import os
from silence_tensorflow import silence_tensorflow

silence_tensorflow()
import tensorflow as tf
from tensorflow.keras.layers import Dense, Activation, Dropout, Input, Conv2D, \
MaxPooling2D, Flatten, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
tf.autograph.set_verbosity(0)
tf.get_logger().setLevel('ERROR')
BATCH_SIZE = 1
IMG_HEIGHT = 32
IMG_WIDTH = 32
IMG_CHANNELS=1

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Class and Functions
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
class AdverbNet(object):
    def __init__(self):
        self.Model = Sequential()
        self.build()

    def build(self):
    
        model = tf.keras.models.Sequential([
        tf.keras.layers.InputLayer(input_shape=( 1,IMG_HEIGHT, IMG_WIDTH, 1 )),
        tf.keras.layers.Reshape((IMG_HEIGHT, IMG_WIDTH, 1)),
        tf.keras.layers.RandomFlip('horizontal'),
        tf.keras.layers.RandomRotation(0.2),
        tf.keras.layers.Normalization(mean=3., variance=2.),
        tf.keras.layers.Normalization(mean=4., variance=6.),
        tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
        tf.keras.layers.Reshape((30, 30, 32)),
        tf.keras.layers.MaxPooling2D((2, 2)),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Reshape((128, 225)),
        tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96, return_sequences=True, return_state=False)),
        tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96)),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(192, activation='relu'),
        tf.keras.layers.Dense(10),
        ])
        
        self.Model = model
    
        

    def summary(self):
        self.Model.summary()
        
if __name__ == "__main__":
   common_path = ''
   C = AdverbNet()
   ###
   C.Model.compile(optimizer="Adam", loss='SparseCategoricalCrossentropy', metrics=['accuracy'])
   C.Model.summary()
   
with tf.device('/device:GPU:0') :
    batch_size = 16
    epochs = 32
    train_dir = common_path + 'F:\\datasets\\downloads\\Actors\\train'
    test_dir = common_path + 'F:\\datasets\\downloads\\Actors\\validation'
    checkpoint_path = common_path + 'F:\\models\\checkpoint\\temp'
    
train_image_generator = ImageDataGenerator(rescale=1. / 255)  # Generator for  training data generate training and test set
train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,
    directory=train_dir,
    shuffle=True,
    target_size=(32, 32),
    ###class_mode='categorical',
    class_mode='binary',
    ### class_mode='sparse',    # None  # categorical   # binary    # sparse
    color_mode='grayscale')
    
test_image_generator = ImageDataGenerator(rescale=1. / 255)  # Generator for  test data
test_data_gen = test_image_generator.flow_from_directory(batch_size=batch_size,
    directory=test_dir,
    shuffle=False,
    target_size=(32, 32),
    ###class_mode='categorical',
    class_mode='binary',
    ### class_mode='sparse',    # None  # categorical   # binary    # sparse
    color_mode='grayscale')

callback = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy',
                                                patience=10,
                                                restore_best_weights=True,
                                                baseline=0.45)
history = C.Model.fit(train_data_gen,
                          steps_per_epoch=8916,  # Number of images // Batch size
    epochs=epochs,
    verbose=1,
    validation_data=test_data_gen,
    validation_steps=187,
    callbacks=[callback])

C.Model.save(checkpoint_path, save_format='tf')

输出：

Model: "sequential_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #
=================================================================
 reshape (Reshape)           (None, 32, 32, 1)         0

 random_flip (RandomFlip)    (None, 32, 32, 1)         0

 random_rotation (RandomRota  (None, 32, 32, 1)        0
 tion)

 normalization (Normalizatio  (None, 32, 32, 1)        0
 n)

 normalization_1 (Normalizat  (None, 32, 32, 1)        0
 ion)

 conv2d (Conv2D)             (None, 30, 30, 32)        320

 reshape_1 (Reshape)         (None, 30, 30, 32)        0

 max_pooling2d (MaxPooling2D  (None, 15, 15, 32)       0
 )

 dense (Dense)               (None, 15, 15, 128)       4224

 reshape_2 (Reshape)         (None, 128, 225)          0

 bidirectional (Bidirectiona  (None, 128, 192)         247296
 l)

 bidirectional_1 (Bidirectio  (None, 192)              221952
 nal)

 flatten (Flatten)           (None, 192)               0

 dense_1 (Dense)             (None, 192)               37056

 dense_2 (Dense)             (None, 10)                1930

=================================================================
Total params: 512,778
Trainable params: 512,778
Non-trainable params: 0
_________________________________________________________________
Found 46 images belonging to 2 classes.
Found 33 images belonging to 2 classes.
Epoch 1/32
8916/8916 [==============================] - 13s 459us/step - loss: 4.1638 - accuracy: 0.2826 - val_loss: 0.7410 - val_accuracy: 0.5455