没有为变量张量流提供梯度

Question

Whenever I run this script I get the same error. I thought that it might be I needed to add labels to the fit function but the format my data is in is 'keras.utils.Sequence'. I was thinking there might be something wrong with my model, as this is my first one. Here is my code:

import tensorflow as tf
from keras.metrics import sparse_categorical_accuracy
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Dense, Flatten, BatchNormalization, Conv2D, MaxPool2D, PReLU
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import categorical_crossentropy
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

inputShape = (178, 218, 3)
model = Sequential([
    Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding='same', input_shape=(224, 224, 3)),
    MaxPool2D(pool_size=(2, 2), strides=2),
    Conv2D(filters=64, kernel_size=(3, 3), activation='relu', padding='same', input_shape=(224, 224, 3)),
    MaxPool2D(pool_size=(2, 2), strides=2),
    Flatten(),
    Dense(units=2, activation='softmax')
])

train_path = r'D:\Coding\pythonProject\kerasandtensorflowtutorial\Faces\train'
valid_path = r'D:\Coding\pythonProject\kerasandtensorflowtutorial\Faces\valid'
test_path = r'D:\Coding\pythonProject\kerasandtensorflowtutorial\Faces\test'

train_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
    .flow_from_directory(directory=train_path, target_size=(178, 218), classes=['faces', 'others'], batch_size=10)
valid_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
    .flow_from_directory(directory=valid_path, target_size=(178, 218), classes=['faces', 'others'], batch_size=10)
test_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
    .flow_from_directory(directory=test_path, target_size=(178, 218), classes=['faces', 'others'], batch_size=10, shuffle=False)

print(valid_batches.image_shape)


model.compile(optimizer=Adam(learning_rate=0.0001), loss=sparse_categorical_accuracy, metrics=['accuracy'])
model.fit(x=train_batches, y=train_path, validation_data=valid_batches, epochs=100, verbose=2, batch_size=20)
model.save('model/face.h5')

And here is the full error message I get:

Traceback (most recent call last):
  File "D:/Coding/pythonProject/kerasandtensorflowtutorial/face detection.py", line 37, in <module>
    model.fit(x=train_batches, validation_data=valid_batches, epochs=100, verbose=2, batch_size=20)
  File "C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1183, in fit
    tmp_logs = self.train_function(iterator)
  File "C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\eager\def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\eager\def_function.py", line 933, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\eager\def_function.py", line 764, in _initialize
    *args, **kwds))
  File "C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\eager\function.py", line 3050, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\eager\function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\eager\function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\framework\func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\eager\def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\framework\func_graph.py", line 986, in wrapper
    raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:

    C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\keras\engine\training.py:855 train_function  *
        return step_function(self, iterator)
    C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\keras\engine\training.py:845 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1285 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2833 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3608 _call_for_each_replica
        return fn(*args, **kwargs)
    C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\keras\engine\training.py:838 run_step  **
        outputs = model.train_step(data)
    C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\keras\engine\training.py:799 train_step
        self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
    C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\keras\optimizer_v2\optimizer_v2.py:530 minimize
        return self.apply_gradients(grads_and_vars, name=name)
    C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\keras\optimizer_v2\optimizer_v2.py:630 apply_gradients
        grads_and_vars = optimizer_utils.filter_empty_gradients(grads_and_vars)
    C:\Users\h\.conda\envs\AI2\lib\site-packages\tensorflow\python\keras\optimizer_v2\utils.py:76 filter_empty_gradients
        ([v.name for _, v in grads_and_vars],))

    ValueError: No gradients provided for any variable: ['conv2d/kernel:0', 'conv2d/bias:0', 'conv2d_1/kernel:0', 'conv2d_1/bias:0', 'dense/kernel:0', 'dense/bias:0'].

Answer 1

我需要将损失更改为“categorical_crossentropy”