ValueError: Shapes (None, None) and (None, 28, 28, 12) are incompatible
ValueError: Shapes (None, None) and (None, 28, 28, 12) are incompatible
İ 正在处理分类为 12 类 的图像数据集。我正在使用 VGG16 的迁移学习。但是，İ 遇到了错误：Shapes (None, None) 和 (None, 28, 28, 12) 不兼容。
我的代码：
IMAGE_SHAPE = (224, 224)
BATCH_SIZE = 32
train = ImageDataGenerator()
train_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale= 1./255, fill_mode= 'nearest')
train_data = train_generator.flow_from_directory(directory="path",target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical', batch_size=BATCH_SIZE , shuffle = True )
valid = ImageDataGenerator()
validation_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
valid_data = validation_generator.flow_from_directory(directory="path, target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical' , batch_size=BATCH_SIZE , shuffle = True )
test = ImageDataGenerator()
test_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
test_data = test_generator.flow_from_directory(directory='path',target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical' , batch_size=1 , shuffle = False )
test_data.reset()
from keras.applications.vgg19 import VGG19
vggmodel = VGG19(weights='imagenet', include_top=True)
for layers in (vggmodel.layers)[:32]:
print(layers)
layers.trainable = False
import tensorflow as tf
tf.keras.Model
tf.keras.layers.Dense
from keras import optimizers
tf.keras.preprocessing.image.ImageDataGenerator
tf.keras.preprocessing.image.load_img
X= vggmodel.layers[-12].output
flatten = tf.keras.layers.Flatten()(X)
out = vggmodel.layers(flatten)
predictions = tf.keras.layers.Dense(num_classes, activation='softmax')
model_final = Model(vggmodel.input, predictions)
model_final.compile(optimizer = optimizers.Adam(lr=0.0003), loss='categorical_crossentropy', metrics=["accuracy"])
for image_batch, labels_batch in train_data:
print(image_batch.shape)
print(labels_batch.shape)
break
from keras.callbacks import ModelCheckpoint, EarlyStopping
checkpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
early = EarlyStopping(monitor='val_acc',patience=40, verbose=1, mode='auto')
model_final.fit_generator(generator= train_data, steps_per_epoch= 2, epochs= 100, validation_data= valid_data, validation_steps=1, callbacks=[checkpoint,early])
model_final.save_weights("vgg16_1.h5")
错误：ValueError: Shapes (None, None) and (None, 28, 28, 12) are incompatible[![在此处输入图片描述]
错误详情：
ValueError Traceback (most recent call last)
<ipython-input-39-938295cc34c4> in <module>()
2 checkpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
3 early = EarlyStopping(monitor='val_acc', min_delta=0, patience=40, verbose=1, mode='auto')
----> 4 model_final.fit_generator(generator= train_images , steps_per_epoch= 2, epochs= 100, validation_data= val_images , validation_steps=1, callbacks=[checkpoint,early])
5 model_final.save_weights("vgg16_1.h5")
10 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
984 except Exception as e: # pylint:disable=broad-except
985 if hasattr(e, "ag_error_metadata"):
--> 986 raise e.ag_error_metadata.to_exception(e)
987 else:
988 raise
ValueError: in user code:
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:830 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:813 run_step *
outputs = model.train_step(data)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:771 train_step *
loss = self.compiled_loss(
/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py:201 __call__ *
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:142 __call__ *
losses = call_fn(y_true, y_pred)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:246 call *
return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper **
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:1631 categorical_crossentropy
y_true, y_pred, from_logits=from_logits)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/backend.py:4827 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py:1161 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, None) and (None, 28, 28, 12) are incompatible
1]1
更新代码：
image_dir = Path('../content/dataset')
# Get filepaths and labels
filepaths = list(image_dir.glob(r'**/*.png'))
labels = list(map(lambda x: os.path.split(os.path.split(x)[0])[1], filepaths))
def create_gen():
# Load the Images with a generator and Data Augmentation
train_generator = tf.keras.preprocessing.image.ImageDataGenerator(
preprocessing_function=tf.keras.applications.vgg16.preprocess_input,
validation_split=0.1
)
test_generator = tf.keras.preprocessing.image.ImageDataGenerator(
preprocessing_function=tf.keras.applications.vgg16.preprocess_input
)
train_images = train_generator.flow_from_dataframe(
dataframe=train_df,
x_col='Filepath',
y_col='Label',
target_size=(224, 224),
color_mode='rgb',
class_mode='categorical',
batch_size=32,
shuffle=True,
seed=0,
subset='training',
rotation_range=30, # Uncomment to use data augmentation
zoom_range=0.15,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
horizontal_flip=True,
fill_mode="nearest"
)
val_images = train_generator.flow_from_dataframe(
dataframe=train_df,
x_col='Filepath',
y_col='Label',
target_size=(224, 224),
color_mode='rgb',
class_mode='categorical',
batch_size=32,
shuffle=True,
seed=0,
subset='validation',
rotation_range=30, # Uncomment to use data augmentation
zoom_range=0.15,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
horizontal_flip=True,
fill_mode="nearest"
)
test_images = test_generator.flow_from_dataframe(
dataframe=test_df,
x_col='Filepath',
y_col='Label',
target_size=(224, 224),
color_mode='rgb',
class_mode='categorical',
batch_size=32,
shuffle=False
)
return train_generator,test_generator,train_images,val_images,test_images
from keras.applications.vgg16 import VGG16
vggmodel = VGG16(weights='imagenet', include_top=True)
for layers in (vggmodel.layers)[:256]:
print(layers)
layers.trainable = False
X= vggmodel.layers[-12].output
predictions = Dense(12, activation="softmax")(X)
model_final = Model(vggmodel.input, predictions)
model_final.compile(optimizer = optimizers.Adam(lr=0.0003), loss='categorical_crossentropy', metrics=["accuracy"])
# Separate in train and test data
train_df, test_df = train_test_split(image_df, train_size=0.9, shuffle=True, random_state=1)
# Create the generators
train_generator,test_generator,train_images,val_images,test_images = create_gen()
from keras.callbacks import ModelCheckpoint, EarlyStopping
checkpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
early = EarlyStopping(monitor='val_acc', min_delta=0, patience=40, verbose=1, mode='auto')
model_final.fit_generator(generator= train_images, steps_per_epoch= 2, epochs= 100, validation_data= val_images, validation_steps=1, callbacks=[checkpoint,early])
model_final.save_weights("vgg16_1.h5")
详细信息错误：
valueError Traceback (most recent call last)
<ipython-input-56-5210d7f2da32> in <module>()
2 checkpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
3 early = EarlyStopping(monitor='val_acc', min_delta=0, patience=40, verbose=1, mode='auto')
----> 4 model_final.fit_generator(generator= train_images, steps_per_epoch= 2, epochs= 100, validation_data= val_images, validation_steps=1, callbacks=[checkpoint,early])
5 model_final.save_weights("vgg16_1.h5")
10 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
984 except Exception as e: # pylint:disable=broad-except
985 if hasattr(e, "ag_error_metadata"):
--> 986 raise e.ag_error_metadata.to_exception(e)
987 else:
988 raise
ValueError: in user code:
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:830 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:813 run_step *
outputs = model.train_step(data)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:771 train_step *
loss = self.compiled_loss(
/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py:201 __call__ *
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:142 __call__ *
losses = call_fn(y_true, y_pred)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:246 call *
return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper **
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:1631 categorical_crossentropy
y_true, y_pred, from_logits=from_logits)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/backend.py:4827 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py:1161 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, None) and (None, 28, 28, 12) are incompatible
你的代码有很多小错误：
- 您在使用生成器时使用的是字符串
path 而不是变量 path。
- 训练路径、验证路径和测试路径也应该不同。
- 您没有为 VGG19 型号指定
input_tensor。
你的代码应该是这样的：
#train_dir_path is path to your training images
train_data = train_generator.flow_from_directory(directory=train_dir_path,target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical', batch_size=BATCH_SIZE , shuffle = True )
#valid_dir_path is path to your validation images
valid_data = validation_generator.flow_from_directory(directory=valid_dir_path, target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical' , batch_size=BATCH_SIZE , shuffle = True )
- VGG 模型的输出在传递到 Dense 层之前应该被展平
完整代码：
IMAGE_SHAPE = (224, 224)
BATCH_SIZE = 32
train = ImageDataGenerator()
train_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale= 1./255, fill_mode= 'nearest')
#train_dir_path is path to your training images
train_data = train_generator.flow_from_directory(directory=train_dir_path,target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical', batch_size=BATCH_SIZE , shuffle = True )
valid = ImageDataGenerator()
validation_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
#valid_dir_path is path to your validation images
valid_data = validation_generator.flow_from_directory(directory=valid_dir_path, target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical' , batch_size=BATCH_SIZE , shuffle = True )
test = ImageDataGenerator()
test_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
#test_dir_path is path to your test images
test_data = test_generator.flow_from_directory(directory=test_dir_path,target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical' , batch_size=1 , shuffle = False )
test_data.reset()
from keras.applications.vgg19 import VGG19
vggmodel = VGG19(weights='imagenet', include_top=True, input_tensor=tensorflow.keras.layers.Input(shape=(224,224,3)))
for layers in (vggmodel.layers)[:32]:
print(layers)
layers.trainable = False
X= vggmodel.layers[-12].output
X = tensorflow.keras.layers.Flatten()(X)
predictions = Dense(12, activation="softmax")(X)
model_final = Model(vggmodel.input, predictions)
model_final.compile(optimizer = optimizers.Adam(lr=0.0003), loss='categorical_crossentropy', metrics=["accuracy"])
for image_batch, labels_batch in train_data:
print(image_batch.shape)
print(labels_batch.shape)
break
from keras.callbacks import ModelCheckpoint, EarlyStopping
checkpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
early = EarlyStopping(monitor='val_acc',patience=40, verbose=1, mode='auto')
model_final.fit_generator(generator= train_data, steps_per_epoch= 2, epochs= 100, validation_data= valid_data, validation_steps=1, callbacks=[checkpoint,early])
model_final.save_weights("vgg16_1.h5")
İ 正在处理分类为 12 类 的图像数据集。我正在使用 VGG16 的迁移学习。但是，İ 遇到了错误：Shapes (None, None) 和 (None, 28, 28, 12) 不兼容。 我的代码：
IMAGE_SHAPE = (224, 224)
BATCH_SIZE = 32
train = ImageDataGenerator()
train_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale= 1./255, fill_mode= 'nearest')
train_data = train_generator.flow_from_directory(directory="path",target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical', batch_size=BATCH_SIZE , shuffle = True )
valid = ImageDataGenerator()
validation_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
valid_data = validation_generator.flow_from_directory(directory="path, target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical' , batch_size=BATCH_SIZE , shuffle = True )
test = ImageDataGenerator()
test_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
test_data = test_generator.flow_from_directory(directory='path',target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical' , batch_size=1 , shuffle = False )
test_data.reset()
from keras.applications.vgg19 import VGG19
vggmodel = VGG19(weights='imagenet', include_top=True)
for layers in (vggmodel.layers)[:32]:
print(layers)
layers.trainable = False
import tensorflow as tf
tf.keras.Model
tf.keras.layers.Dense
from keras import optimizers
tf.keras.preprocessing.image.ImageDataGenerator
tf.keras.preprocessing.image.load_img
X= vggmodel.layers[-12].output
flatten = tf.keras.layers.Flatten()(X)
out = vggmodel.layers(flatten)
predictions = tf.keras.layers.Dense(num_classes, activation='softmax')
model_final = Model(vggmodel.input, predictions)
model_final.compile(optimizer = optimizers.Adam(lr=0.0003), loss='categorical_crossentropy', metrics=["accuracy"])
for image_batch, labels_batch in train_data:
print(image_batch.shape)
print(labels_batch.shape)
break
from keras.callbacks import ModelCheckpoint, EarlyStopping
checkpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
early = EarlyStopping(monitor='val_acc',patience=40, verbose=1, mode='auto')
model_final.fit_generator(generator= train_data, steps_per_epoch= 2, epochs= 100, validation_data= valid_data, validation_steps=1, callbacks=[checkpoint,early])
model_final.save_weights("vgg16_1.h5")
错误：ValueError: Shapes (None, None) and (None, 28, 28, 12) are incompatible[![在此处输入图片描述]
错误详情：
ValueError Traceback (most recent call last)
<ipython-input-39-938295cc34c4> in <module>()
2 checkpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
3 early = EarlyStopping(monitor='val_acc', min_delta=0, patience=40, verbose=1, mode='auto')
----> 4 model_final.fit_generator(generator= train_images , steps_per_epoch= 2, epochs= 100, validation_data= val_images , validation_steps=1, callbacks=[checkpoint,early])
5 model_final.save_weights("vgg16_1.h5")
10 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
984 except Exception as e: # pylint:disable=broad-except
985 if hasattr(e, "ag_error_metadata"):
--> 986 raise e.ag_error_metadata.to_exception(e)
987 else:
988 raise
ValueError: in user code:
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:830 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:813 run_step *
outputs = model.train_step(data)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:771 train_step *
loss = self.compiled_loss(
/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py:201 __call__ *
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:142 __call__ *
losses = call_fn(y_true, y_pred)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:246 call *
return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper **
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:1631 categorical_crossentropy
y_true, y_pred, from_logits=from_logits)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/backend.py:4827 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py:1161 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, None) and (None, 28, 28, 12) are incompatible
1]1
更新代码：
image_dir = Path('../content/dataset')
# Get filepaths and labels
filepaths = list(image_dir.glob(r'**/*.png'))
labels = list(map(lambda x: os.path.split(os.path.split(x)[0])[1], filepaths))
def create_gen():
# Load the Images with a generator and Data Augmentation
train_generator = tf.keras.preprocessing.image.ImageDataGenerator(
preprocessing_function=tf.keras.applications.vgg16.preprocess_input,
validation_split=0.1
)
test_generator = tf.keras.preprocessing.image.ImageDataGenerator(
preprocessing_function=tf.keras.applications.vgg16.preprocess_input
)
train_images = train_generator.flow_from_dataframe(
dataframe=train_df,
x_col='Filepath',
y_col='Label',
target_size=(224, 224),
color_mode='rgb',
class_mode='categorical',
batch_size=32,
shuffle=True,
seed=0,
subset='training',
rotation_range=30, # Uncomment to use data augmentation
zoom_range=0.15,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
horizontal_flip=True,
fill_mode="nearest"
)
val_images = train_generator.flow_from_dataframe(
dataframe=train_df,
x_col='Filepath',
y_col='Label',
target_size=(224, 224),
color_mode='rgb',
class_mode='categorical',
batch_size=32,
shuffle=True,
seed=0,
subset='validation',
rotation_range=30, # Uncomment to use data augmentation
zoom_range=0.15,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
horizontal_flip=True,
fill_mode="nearest"
)
test_images = test_generator.flow_from_dataframe(
dataframe=test_df,
x_col='Filepath',
y_col='Label',
target_size=(224, 224),
color_mode='rgb',
class_mode='categorical',
batch_size=32,
shuffle=False
)
return train_generator,test_generator,train_images,val_images,test_images
from keras.applications.vgg16 import VGG16
vggmodel = VGG16(weights='imagenet', include_top=True)
for layers in (vggmodel.layers)[:256]:
print(layers)
layers.trainable = False
X= vggmodel.layers[-12].output
predictions = Dense(12, activation="softmax")(X)
model_final = Model(vggmodel.input, predictions)
model_final.compile(optimizer = optimizers.Adam(lr=0.0003), loss='categorical_crossentropy', metrics=["accuracy"])
# Separate in train and test data
train_df, test_df = train_test_split(image_df, train_size=0.9, shuffle=True, random_state=1)
# Create the generators
train_generator,test_generator,train_images,val_images,test_images = create_gen()
from keras.callbacks import ModelCheckpoint, EarlyStopping
checkpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
early = EarlyStopping(monitor='val_acc', min_delta=0, patience=40, verbose=1, mode='auto')
model_final.fit_generator(generator= train_images, steps_per_epoch= 2, epochs= 100, validation_data= val_images, validation_steps=1, callbacks=[checkpoint,early])
model_final.save_weights("vgg16_1.h5")
详细信息错误：
valueError Traceback (most recent call last)
<ipython-input-56-5210d7f2da32> in <module>()
2 checkpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
3 early = EarlyStopping(monitor='val_acc', min_delta=0, patience=40, verbose=1, mode='auto')
----> 4 model_final.fit_generator(generator= train_images, steps_per_epoch= 2, epochs= 100, validation_data= val_images, validation_steps=1, callbacks=[checkpoint,early])
5 model_final.save_weights("vgg16_1.h5")
10 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
984 except Exception as e: # pylint:disable=broad-except
985 if hasattr(e, "ag_error_metadata"):
--> 986 raise e.ag_error_metadata.to_exception(e)
987 else:
988 raise
ValueError: in user code:
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:830 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:813 run_step *
outputs = model.train_step(data)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:771 train_step *
loss = self.compiled_loss(
/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py:201 __call__ *
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:142 __call__ *
losses = call_fn(y_true, y_pred)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:246 call *
return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper **
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:1631 categorical_crossentropy
y_true, y_pred, from_logits=from_logits)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/backend.py:4827 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py:1161 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, None) and (None, 28, 28, 12) are incompatible
你的代码有很多小错误：
- 您在使用生成器时使用的是字符串
path而不是变量path。 - 训练路径、验证路径和测试路径也应该不同。
- 您没有为 VGG19 型号指定
input_tensor。
你的代码应该是这样的：
#train_dir_path is path to your training images
train_data = train_generator.flow_from_directory(directory=train_dir_path,target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical', batch_size=BATCH_SIZE , shuffle = True )
#valid_dir_path is path to your validation images
valid_data = validation_generator.flow_from_directory(directory=valid_dir_path, target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical' , batch_size=BATCH_SIZE , shuffle = True )
- VGG 模型的输出在传递到 Dense 层之前应该被展平
完整代码：
IMAGE_SHAPE = (224, 224)
BATCH_SIZE = 32
train = ImageDataGenerator()
train_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale= 1./255, fill_mode= 'nearest')
#train_dir_path is path to your training images
train_data = train_generator.flow_from_directory(directory=train_dir_path,target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical', batch_size=BATCH_SIZE , shuffle = True )
valid = ImageDataGenerator()
validation_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
#valid_dir_path is path to your validation images
valid_data = validation_generator.flow_from_directory(directory=valid_dir_path, target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical' , batch_size=BATCH_SIZE , shuffle = True )
test = ImageDataGenerator()
test_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
#test_dir_path is path to your test images
test_data = test_generator.flow_from_directory(directory=test_dir_path,target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical' , batch_size=1 , shuffle = False )
test_data.reset()
from keras.applications.vgg19 import VGG19
vggmodel = VGG19(weights='imagenet', include_top=True, input_tensor=tensorflow.keras.layers.Input(shape=(224,224,3)))
for layers in (vggmodel.layers)[:32]:
print(layers)
layers.trainable = False
X= vggmodel.layers[-12].output
X = tensorflow.keras.layers.Flatten()(X)
predictions = Dense(12, activation="softmax")(X)
model_final = Model(vggmodel.input, predictions)
model_final.compile(optimizer = optimizers.Adam(lr=0.0003), loss='categorical_crossentropy', metrics=["accuracy"])
for image_batch, labels_batch in train_data:
print(image_batch.shape)
print(labels_batch.shape)
break
from keras.callbacks import ModelCheckpoint, EarlyStopping
checkpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
early = EarlyStopping(monitor='val_acc',patience=40, verbose=1, mode='auto')
model_final.fit_generator(generator= train_data, steps_per_epoch= 2, epochs= 100, validation_data= valid_data, validation_steps=1, callbacks=[checkpoint,early])
model_final.save_weights("vgg16_1.h5")