Tensorflow ValueError:没有为任何变量提供渐变 [英] Tensorflow ValueError: No gradients provided for any variable
问题描述
我正在尝试使我的tensorflow模型在2类图像上进行训练,但遇到了ValueError问题.有人可以帮忙吗? 这是相关代码:
I'm trying to get my tensorflow model to train on 2 categories of images but I'm running into a ValueError problem. Can somebody please help. Here is the relevant code:
# Get image arrays and labels for all image files
images, labels = load_data(sys.argv[1])
# Split data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(
images, labels, test_size=TEST_SIZE
)
# Get a compiled neural network
model = get_model()
model.summary()
# Fit model on training data
model.fit_generator(x_train, steps_per_epoch=128, epochs=EPOCHS,
validation_data=y_train, validation_steps=128)
def load_data(data_dir):
image_generator = ImageDataGenerator(rescale=1. / 255)
resized_imgs = image_generator.flow_from_directory(batch_size=128, directory=data_dir,
shuffle=True, target_size=dimensions,
class_mode='binary')
images, labels = next(resized_imgs)
plotImages(images[:15])
return images, labels
def get_model():
# create a convolutional neural network
model = tf.keras.models.Sequential([
# convolutional layer. Learn 32 filters using
a 3x3 kernel
tf.keras.layers.Conv2D(
32, (3, 3), activation="relu", input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)
),
tf.keras.layers.BatchNormalization(),
# max-pooling layer, using 2x2 pool size
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
# convolutional layer. Learn 32 filters using a 3x3 kernel
tf.keras.layers.Conv2D(
32, (3, 3), activation="relu", input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)
),
tf.keras.layers.BatchNormalization(),
# max-pooling layer, using 2x2 pool size
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
# flatten units
tf.keras.layers.Flatten(),
# add a hidden layer with dropout
tf.keras.layers.Dense(128, activation="relu"),
tf.keras.layers.Dropout(0.5),
# add an output layer with NUM_CATEGORIES (43) units
tf.keras.layers.Dense(NUM_CATEGORIES, activation="sigmoid") # changed activation from softmax
# to sigmoid whic is the proper activation for binary data
])
# train neural network
model.compile(
optimizer="adam",
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics=["accuracy"]
)
return model
我最终收到以下错误: ValueError:没有为任何变量提供渐变:['conv2d/kernel:0','conv2d/bias:0','batch_normalization/gamma:0','batch_normalization/beta:0','conv2d_1/kernel:0', 'conv2d_1/bias:0','batch_normalization_1/gamma:0','batch_normalization_1/beta:0','dense/kernel:0','dense/bias:0','dense_1/kernel:0','dense_1 /bias:0'].
I end up getting the following error: ValueError: No gradients provided for any variable: ['conv2d/kernel:0', 'conv2d/bias:0', 'batch_normalization/gamma:0', 'batch_normalization/beta:0', 'conv2d_1/kernel:0', 'conv2d_1/bias:0', 'batch_normalization_1/gamma:0', 'batch_normalization_1/beta:0', 'dense/kernel:0', 'dense/bias:0', 'dense_1/kernel:0', 'dense_1/bias:0'].
该错误来自以下代码行,但不确定如何解决:
The error is coming from the following line of code but not sure how to fix it:
model.fit_generator(x_train, steps_per_epoch=128, epochs=EPOCHS,
validation_data=y_train, validation_steps=128)
谢谢
推荐答案
弄清楚了.由于tf模型中的最终输出层,我的logit与标签形状不匹配.
Figured it out. My logits weren't matching my label shape because of the final output layer in my tf model.
NUM_CATEGORIES = 2
tf.keras.layers.Dense(NUM_CATEGORIES, activation="sigmoid")
我将单位设置为2而不是1,所以我的输出形状是(None,2)而不是(None,1)
I had the units set to 2 instead of 1, so my output shape was (None, 2) instead of (None, 1)
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