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[英]How to fix 'ValueError: Empty Training Data' error in tensorflow
[英]How to fix the error when data augmenting TensorFlow training data?
我正在嘗試數據增強我的 TensorFlow 模型的訓練數據。 我的模型在沒有數據增強的情況下運行。 我想增加訓練數據以改善結果。 這是我的嘗試:
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
directory_testData,
#validation_split=0.2,
target_size=(150, 150),
batch_size=32,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
directory_testData,
#validation_split=0.2,
target_size=(150, 150),
batch_size=32,
class_mode='binary')
model = create_functionalModel()
model.fit(
train_generator,
steps_per_epoch=2000,
epochs=50,
validation_data=validation_generator,
validation_steps=800)
然后我運行它並收到這些錯誤:
Model: "model_4"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_5 (InputLayer) [(None, 180, 180, 3)] 0
rescaling_5 (Rescaling) (None, 180, 180, 3) 0
conv2d_12 (Conv2D) (None, 180, 180, 16) 448
max_pooling2d_12 (MaxPoolin (None, 90, 90, 16) 0
g2D)
conv2d_13 (Conv2D) (None, 90, 90, 32) 4640
max_pooling2d_13 (MaxPoolin (None, 45, 45, 32) 0
g2D)
conv2d_14 (Conv2D) (None, 45, 45, 64) 18496
max_pooling2d_14 (MaxPoolin (None, 22, 22, 64) 0
g2D)
flatten_4 (Flatten) (None, 30976) 0
dense_8 (Dense) (None, 128) 3965056
dense_9 (Dense) (None, 3) 387
=================================================================
Total params: 3,989,027
Trainable params: 3,989,027
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/50
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-25-1c89b4a3fc84> in <module>()
30 epochs=50,
31 validation_data=validation_generator,
---> 32 validation_steps=800)
1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
57 ctx.ensure_initialized()
58 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 59 inputs, attrs, num_outputs)
60 except core._NotOkStatusException as e:
61 if name is not None:
InvalidArgumentError: Input to reshape is a tensor with 663552 values, but the requested shape requires a multiple of 30976
[[node model_4/flatten_4/Reshape
(defined at /usr/local/lib/python3.7/dist-packages/keras/layers/core/flatten.py:96)
]] [Op:__inference_train_function_4555]
這個問題似乎與我的模型所需的輸入形狀有關。 你能幫我理解如何解決這個問題嗎? 謝謝你。
編輯:
From tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
directory_testData,
#validation_split=0.2,
target_size=(180, 180),
batch_size=32,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
directory_testData,
#validation_split=0.2,
target_size=(180, 180),
batch_size=32,
class_mode='binary')
model = create_functionalModel()
model.fit(
train_generator,
steps_per_epoch=30,
epochs=50,
validation_data=validation_generator,
validation_steps=800)
您的模型需要輸入形狀 (180, 180) 但是您正在將圖像大小調整為 (150, 150)。
改變:
target_size=(150, 150),
到:
target_size=(180, 180),
應該修復它。
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