训练 CNN 模型（tensorflow）时出错
Getting error when training the CNN model(tensorflow)
我在训练我的 CNN 模型时遇到错误，该模型用于检查一个人用手语讲的内容。我正在使用 keras，tensorflow。
这是我的代码：
import tensorflow as tf ;importing libraries
from tensorflow.keras import datasets,layers,models
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator
; Data preprocessing
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
training_set = train_datagen.flow_from_directory('split__data/Train',
target_size = (64, 64),
batch_size = 32,
class_mode = 'categorical')
test_datagen = ImageDataGenerator(rescale = 1./255)
test_set = test_datagen.flow_from_directory('split__data/Test',
target_size = (64, 64),
batch_size = 32,
class_mode = 'categorical')
; Building the model
cnn = tf.keras.models.Sequential()
cnn.add(tf.keras.layers.Conv2D(filters = 16,kernel_size = 3,activation = 'relu',input_shape = [64,64,3]))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))
cnn.add(tf.keras.layers.Conv2D(filters = 32,kernel_size = 3,activation = 'relu',input_shape = [64,64,3]))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))
cnn.add(tf.keras.layers.Conv2D(filters = 64,kernel_size = 3,activation = 'relu',input_shape = [64,64,3]))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))
cnn.add(tf.keras.layers.Flatten())
cnn.add(tf.keras.layers.Dense(units=500,activation='relu'))
cnn.add(tf.keras.layers.Dense(units=1,activation='softmax'))
; compiling and training
cnn.compile(optimizer = 'adam' , loss= 'categorical_crossentropy',metrics = ['accuracy'])
;the next line is giving me error
cnn.fit(x = training_set,validation_data = test_set,batch_size = 32,epochs = 10)
这是错误。我无法从生成这些错误的位置获取信息。我试过更改批量大小、图像高度和宽度，但没有任何反应。我遇到了同样的错误。
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-15-b67ba9813850> in <module>
----> 1 cnn.fit(x = training_set,validation_data = test_set,batch_size = 32,epochs = 10)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1181 _r=1):
1182 callbacks.on_train_batch_begin(step)
-> 1183 tmp_logs = self.train_function(iterator)
1184 if data_handler.should_sync:
1185 context.async_wait()
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
887
888 with OptionalXlaContext(self._jit_compile):
--> 889 result = self._call(*args, **kwds)
890
891 new_tracing_count = self.experimental_get_tracing_count()
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
948 # Lifting succeeded, so variables are initialized and we can run the
949 # stateless function.
--> 950 return self._stateless_fn(*args, **kwds)
951 else:
952 _, _, _, filtered_flat_args = \
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\function.py in __call__(self, *args, **kwargs)
3021 (graph_function,
3022 filtered_flat_args) = self._maybe_define_function(args, kwargs)
-> 3023 return graph_function._call_flat(
3024 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access
3025
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1958 and executing_eagerly):
1959 # No tape is watching; skip to running the function.
-> 1960 return self._build_call_outputs(self._inference_function.call(
1961 ctx, args, cancellation_manager=cancellation_manager))
1962 forward_backward = self._select_forward_and_backward_functions(
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\function.py in call(self, ctx, args, cancellation_manager)
589 with _InterpolateFunctionError(self):
590 if cancellation_manager is None:
--> 591 outputs = execute.execute(
592 str(self.signature.name),
593 num_outputs=self._num_outputs,
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
57 try:
58 ctx.ensure_initialized()
---> 59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
InvalidArgumentError: In[0] mismatch In[1] shape: 35 vs. 1: [32,35] [500,1] 0 0
[[node gradient_tape/sequential/dense_1/MatMul (defined at <ipython-input-15-b67ba9813850>:1) ]] [Op:__inference_train_function_847]
Function call stack:
train_function
有人可以帮忙吗？
数据集中有多少 类？你有代码
cnn.add(tf.keras.layers.Dense(units=1,activation='softmax'))
这表明您正在进行二进制分类，但我认为这不是您想要的。在你的生成器代码之后试试这个
classes=list(training_set.class_indices.keys())
class_count=len (classes) # this integer is the number of nodes you need in your models final layer
将模型的最后一层更改为
cnn.add(tf.keras.layers.Dense(units=class_count,activation='softmax'))
预测单个图像 - 首先读取图像。如果您在 RGB 图像上训练模型，那么
import matplotlib.pyplot as plt
import cv2
classes=list(train_gen.class_indices.keys())
img_path = r' full path to the image'
img=plt.imread(img_path)
img=cv2.resize(img, (64.64))
img=img/255
img=np.expand_dims(img, axis=0)
prediction=model.predict( img, verbose=1)
index=argmax(prediction)
predicted_class = classes(index)
我懒得自己构建 CNN，而是使用 EfficientNetB3 模型进行迁移学习。注意 EfficientNet 需要 0 到 255 范围内的像素，因此不要缩放像素。下面的代码适用于大多数应用程序
base_model=tf.keras.applications.EfficientNetB3(include_top=False, weights="imagenet",input_shape=img_shape, pooling='max')
x=base_model.output
x=keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001 )(x)
x = Dense(256, kernel_regularizer = regularizers.l2(l = 0.016),activity_regularizer=regularizers.l1(0.006),
bias_regularizer=regularizers.l1(0.006) ,activation='relu')(x)
x=Dropout(rate=.45, seed=123)(x)
output=Dense(class_count, activation='softmax')(x)
model=Model(inputs=base_model.input, outputs=output)
model.compile(Adamax(learning_rate=.001), loss='categorical_crossentropy', metrics=['accuracy'])
我在训练我的 CNN 模型时遇到错误，该模型用于检查一个人用手语讲的内容。我正在使用 keras，tensorflow。 这是我的代码：
import tensorflow as tf ;importing libraries
from tensorflow.keras import datasets,layers,models
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator
; Data preprocessing
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
training_set = train_datagen.flow_from_directory('split__data/Train',
target_size = (64, 64),
batch_size = 32,
class_mode = 'categorical')
test_datagen = ImageDataGenerator(rescale = 1./255)
test_set = test_datagen.flow_from_directory('split__data/Test',
target_size = (64, 64),
batch_size = 32,
class_mode = 'categorical')
; Building the model
cnn = tf.keras.models.Sequential()
cnn.add(tf.keras.layers.Conv2D(filters = 16,kernel_size = 3,activation = 'relu',input_shape = [64,64,3]))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))
cnn.add(tf.keras.layers.Conv2D(filters = 32,kernel_size = 3,activation = 'relu',input_shape = [64,64,3]))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))
cnn.add(tf.keras.layers.Conv2D(filters = 64,kernel_size = 3,activation = 'relu',input_shape = [64,64,3]))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))
cnn.add(tf.keras.layers.Flatten())
cnn.add(tf.keras.layers.Dense(units=500,activation='relu'))
cnn.add(tf.keras.layers.Dense(units=1,activation='softmax'))
; compiling and training
cnn.compile(optimizer = 'adam' , loss= 'categorical_crossentropy',metrics = ['accuracy'])
;the next line is giving me error
cnn.fit(x = training_set,validation_data = test_set,batch_size = 32,epochs = 10)
这是错误。我无法从生成这些错误的位置获取信息。我试过更改批量大小、图像高度和宽度，但没有任何反应。我遇到了同样的错误。
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-15-b67ba9813850> in <module>
----> 1 cnn.fit(x = training_set,validation_data = test_set,batch_size = 32,epochs = 10)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1181 _r=1):
1182 callbacks.on_train_batch_begin(step)
-> 1183 tmp_logs = self.train_function(iterator)
1184 if data_handler.should_sync:
1185 context.async_wait()
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
887
888 with OptionalXlaContext(self._jit_compile):
--> 889 result = self._call(*args, **kwds)
890
891 new_tracing_count = self.experimental_get_tracing_count()
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
948 # Lifting succeeded, so variables are initialized and we can run the
949 # stateless function.
--> 950 return self._stateless_fn(*args, **kwds)
951 else:
952 _, _, _, filtered_flat_args = \
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\function.py in __call__(self, *args, **kwargs)
3021 (graph_function,
3022 filtered_flat_args) = self._maybe_define_function(args, kwargs)
-> 3023 return graph_function._call_flat(
3024 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access
3025
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1958 and executing_eagerly):
1959 # No tape is watching; skip to running the function.
-> 1960 return self._build_call_outputs(self._inference_function.call(
1961 ctx, args, cancellation_manager=cancellation_manager))
1962 forward_backward = self._select_forward_and_backward_functions(
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\function.py in call(self, ctx, args, cancellation_manager)
589 with _InterpolateFunctionError(self):
590 if cancellation_manager is None:
--> 591 outputs = execute.execute(
592 str(self.signature.name),
593 num_outputs=self._num_outputs,
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
57 try:
58 ctx.ensure_initialized()
---> 59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
InvalidArgumentError: In[0] mismatch In[1] shape: 35 vs. 1: [32,35] [500,1] 0 0
[[node gradient_tape/sequential/dense_1/MatMul (defined at <ipython-input-15-b67ba9813850>:1) ]] [Op:__inference_train_function_847]
Function call stack:
train_function
有人可以帮忙吗？
数据集中有多少 类？你有代码
cnn.add(tf.keras.layers.Dense(units=1,activation='softmax'))
这表明您正在进行二进制分类，但我认为这不是您想要的。在你的生成器代码之后试试这个
classes=list(training_set.class_indices.keys())
class_count=len (classes) # this integer is the number of nodes you need in your models final layer
将模型的最后一层更改为
cnn.add(tf.keras.layers.Dense(units=class_count,activation='softmax'))
预测单个图像 - 首先读取图像。如果您在 RGB 图像上训练模型，那么
import matplotlib.pyplot as plt
import cv2
classes=list(train_gen.class_indices.keys())
img_path = r' full path to the image'
img=plt.imread(img_path)
img=cv2.resize(img, (64.64))
img=img/255
img=np.expand_dims(img, axis=0)
prediction=model.predict( img, verbose=1)
index=argmax(prediction)
predicted_class = classes(index)
我懒得自己构建 CNN，而是使用 EfficientNetB3 模型进行迁移学习。注意 EfficientNet 需要 0 到 255 范围内的像素，因此不要缩放像素。下面的代码适用于大多数应用程序
base_model=tf.keras.applications.EfficientNetB3(include_top=False, weights="imagenet",input_shape=img_shape, pooling='max')
x=base_model.output
x=keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001 )(x)
x = Dense(256, kernel_regularizer = regularizers.l2(l = 0.016),activity_regularizer=regularizers.l1(0.006),
bias_regularizer=regularizers.l1(0.006) ,activation='relu')(x)
x=Dropout(rate=.45, seed=123)(x)
output=Dense(class_count, activation='softmax')(x)
model=Model(inputs=base_model.input, outputs=output)
model.compile(Adamax(learning_rate=.001), loss='categorical_crossentropy', metrics=['accuracy'])