ValueError: logits and labels must have the same shape ((None, 23, 23, 1) vs (None, 1))
ValueError: logits and labels must have the same shape ((None, 23, 23, 1) vs (None, 1))
我是 ML 的新手，所以我真的不知道我在做什么我不知道代码中的 logits 是什么意思我什至没有写过 logits 我只是按照 YouTube 教程让自己熟悉环境..这是完整的代码，感谢您的帮助..我知道Whosebug上已经有这种post，但我认为它不适用于我的情况，也许我不知道但我仍然不知道如何实现它，即使它这样做了，请帮助我在这里挣扎:) tnx
代码：
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
from tensorflow.keras.optimizers import RMSprop
import matplotlib.pyplot as plt
import tensorflow as tf
import cv2
import os
import numpy as np
img = cv2.imread("/content/drive/MyDrive/data/train/ha/2.jpg").shape
print(img)
imgg = image.load_img("/content/drive/MyDrive/data/train/ha/2.jpg")
plt.imshow(imgg)
train = ImageDataGenerator(rescale=1/255)
validation = ImageDataGenerator(rescale=1/255)
train_dataset = train.flow_from_directory("/content/drive/MyDrive/data/train/", target_size = (100,100),
batch_size = 3,
class_mode ="binary")
print(train_dataset.class_indices)
validation_dataset = train.flow_from_directory("/content/drive/MyDrive/data/validate/",
target_size = (100,100),
batch_size = 3,
class_mode ="binary")
model = tf.keras.models.Sequential([tf.keras.layers.Conv2D(16,(3,3),activation = 'relu',input_shape =(200,200,3)),
tf.keras.layers.MaxPool2D(2,2),
#
tf.keras.layers.Conv2D(32,(3,3),activation = 'relu'),
tf.keras.layers.MaxPool2D(2,2),
#
tf.keras.layers.Conv2D(64,(3,3),activation = 'relu'),
tf.keras.layers.MaxPool2D(2,2),
##
tf.keras.layers.Dense(134,activation = 'relu'),
##
tf.keras.layers.Dense(1,activation = 'sigmoid')
])
model.compile(loss = 'binary_crossentropy',
optimizer = RMSprop(lr=0.001),
metrics =['accuracy'])
model_fit = model.fit(train_dataset,
steps_per_epoch = 3,
epochs = 1,
validation_data = validation_dataset)
错误：
2021-01-01 13:39:18.588397: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
(51, 51, 3)
Found 9 images belonging to 2 classes.
{'ha': 0, 'hu': 1}
Found 4 images belonging to 2 classes.
2021-01-01 13:39:22.999078: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-01 13:39:23.026197: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2021-01-01 13:39:23.092853: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2021-01-01 13:39:23.092917: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (6e4fde799083): /proc/driver/nvidia/version does not exist
2021-01-01 13:39:23.093374: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-01 13:39:23.846859: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2021-01-01 13:39:23.850373: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2300000000 Hz
Traceback (most recent call last):
File "/content/drive/MyDrive/main.py", line 48, in <module>
validation_data = validation_dataset)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 1100, in fit
tmp_logs = self.train_function(iterator)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
result = self._call(*args, **kwds)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py", line 871, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
*args, **kwds))
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
graph_function, _ = self._maybe_define_function(args, kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
out = weak_wrapped_fn().__wrapped__(*args, **kwds)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py", line 977, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:756 train_step
y, y_pred, sample_weight, regularization_losses=self.losses)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:203 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:152 __call__
losses = call_fn(y_true, y_pred)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:256 call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:1608 binary_crossentropy
K.binary_crossentropy(y_true, y_pred, from_logits=from_logits), axis=-1)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py:4979 binary_crossentropy
return nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:174 sigmoid_cross_entropy_with_logits
(logits.get_shape(), labels.get_shape()))
ValueError: logits and labels must have the same shape ((None, 23, 23, 1) vs (None, 1))
你的问题是密集层的输入必须是向量。要做到这一点
you can
replace tf.keras.layers.MaxPool2D(2,2)
with tf.keras.layers.GlobalMaxPooling2D()
或直接添加
tf.keras.layers.GlobalMaxPooling2D() after tf.keras.layers.MaxPool2D(2,2)
我是 ML 的新手，所以我真的不知道我在做什么我不知道代码中的 logits 是什么意思我什至没有写过 logits 我只是按照 YouTube 教程让自己熟悉环境..这是完整的代码，感谢您的帮助..我知道Whosebug上已经有这种post，但我认为它不适用于我的情况，也许我不知道但我仍然不知道如何实现它，即使它这样做了，请帮助我在这里挣扎:) tnx 代码：
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
from tensorflow.keras.optimizers import RMSprop
import matplotlib.pyplot as plt
import tensorflow as tf
import cv2
import os
import numpy as np
img = cv2.imread("/content/drive/MyDrive/data/train/ha/2.jpg").shape
print(img)
imgg = image.load_img("/content/drive/MyDrive/data/train/ha/2.jpg")
plt.imshow(imgg)
train = ImageDataGenerator(rescale=1/255)
validation = ImageDataGenerator(rescale=1/255)
train_dataset = train.flow_from_directory("/content/drive/MyDrive/data/train/", target_size = (100,100),
batch_size = 3,
class_mode ="binary")
print(train_dataset.class_indices)
validation_dataset = train.flow_from_directory("/content/drive/MyDrive/data/validate/",
target_size = (100,100),
batch_size = 3,
class_mode ="binary")
model = tf.keras.models.Sequential([tf.keras.layers.Conv2D(16,(3,3),activation = 'relu',input_shape =(200,200,3)),
tf.keras.layers.MaxPool2D(2,2),
#
tf.keras.layers.Conv2D(32,(3,3),activation = 'relu'),
tf.keras.layers.MaxPool2D(2,2),
#
tf.keras.layers.Conv2D(64,(3,3),activation = 'relu'),
tf.keras.layers.MaxPool2D(2,2),
##
tf.keras.layers.Dense(134,activation = 'relu'),
##
tf.keras.layers.Dense(1,activation = 'sigmoid')
])
model.compile(loss = 'binary_crossentropy',
optimizer = RMSprop(lr=0.001),
metrics =['accuracy'])
model_fit = model.fit(train_dataset,
steps_per_epoch = 3,
epochs = 1,
validation_data = validation_dataset)
错误：
2021-01-01 13:39:18.588397: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
(51, 51, 3)
Found 9 images belonging to 2 classes.
{'ha': 0, 'hu': 1}
Found 4 images belonging to 2 classes.
2021-01-01 13:39:22.999078: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-01 13:39:23.026197: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2021-01-01 13:39:23.092853: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2021-01-01 13:39:23.092917: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (6e4fde799083): /proc/driver/nvidia/version does not exist
2021-01-01 13:39:23.093374: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-01 13:39:23.846859: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2021-01-01 13:39:23.850373: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2300000000 Hz
Traceback (most recent call last):
File "/content/drive/MyDrive/main.py", line 48, in <module>
validation_data = validation_dataset)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 1100, in fit
tmp_logs = self.train_function(iterator)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
result = self._call(*args, **kwds)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py", line 871, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
*args, **kwds))
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
graph_function, _ = self._maybe_define_function(args, kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
out = weak_wrapped_fn().__wrapped__(*args, **kwds)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py", line 977, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:756 train_step
y, y_pred, sample_weight, regularization_losses=self.losses)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:203 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:152 __call__
losses = call_fn(y_true, y_pred)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:256 call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:1608 binary_crossentropy
K.binary_crossentropy(y_true, y_pred, from_logits=from_logits), axis=-1)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py:4979 binary_crossentropy
return nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:174 sigmoid_cross_entropy_with_logits
(logits.get_shape(), labels.get_shape()))
ValueError: logits and labels must have the same shape ((None, 23, 23, 1) vs (None, 1))
你的问题是密集层的输入必须是向量。要做到这一点
you can
replace tf.keras.layers.MaxPool2D(2,2)
with tf.keras.layers.GlobalMaxPooling2D()
或直接添加
tf.keras.layers.GlobalMaxPooling2D() after tf.keras.layers.MaxPool2D(2,2)