how can reslove : InvalidArgumentError: Graph execution error?

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how can reslove : InvalidArgumentError: Graph execution error?

  1. How to solve how can reslove : InvalidArgumentError: Graph execution error?

    You just have to make sure your labels are zero-based starting from 0 to 2, since your output layer has 3 nodes and a softmax activation function and you are using sparse_categorical_crossentropy. Here is a working example:
    import tensorflow as tf model = tf.keras.Sequential([ tf.keras.layers.Conv2D(filters=16, kernel_size=(3,3), activation='relu',input_shape=(256, 256, 3)), tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu'), tf.keras.layers.MaxPool2D(pool_size=(2,2)), tf.keras.layers.BatchNormalization(axis=-1), tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu'), tf.keras.layers.Conv2D(filters=128, kernel_size=(3,3), activation='relu'), tf.keras.layers.MaxPool2D(pool_size=(2,2)), tf.keras.layers.BatchNormalization(axis=-1), tf.keras.layers.Flatten(), tf.keras.layers.Dense(512,activation='relu'), tf.keras.layers.BatchNormalization() , tf.keras.layers.Dropout(rate=0.5), tf.keras.layers.Dense(3,activation='softmax') ]) learning_rate = 0.001 epochs=2 opt= tf.keras.optimizers.Adam(learning_rate=learning_rate , decay=learning_rate/(epochs*0.5)) model.compile(loss='sparse_categorical_crossentropy',optimizer=opt,metrics=['accuracy']) aug = tf.keras.preprocessing.image.ImageDataGenerator( rotation_range=10, zoom_range=0.15, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.15, horizontal_flip= False, vertical_flip= False, fill_mode="nearest" ) X_train = tf.random.normal((50, 256, 256, 3)) y_train = tf.random.uniform((50, ), maxval=3, dtype=tf.int32) history = model.fit(aug.flow(X_train, y_train, batch_size=2), epochs=epochs)
    Use the dummy data as an orientation for your real data.

  2. how can reslove : InvalidArgumentError: Graph execution error?

    You just have to make sure your labels are zero-based starting from 0 to 2, since your output layer has 3 nodes and a softmax activation function and you are using sparse_categorical_crossentropy. Here is a working example:
    import tensorflow as tf model = tf.keras.Sequential([ tf.keras.layers.Conv2D(filters=16, kernel_size=(3,3), activation='relu',input_shape=(256, 256, 3)), tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu'), tf.keras.layers.MaxPool2D(pool_size=(2,2)), tf.keras.layers.BatchNormalization(axis=-1), tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu'), tf.keras.layers.Conv2D(filters=128, kernel_size=(3,3), activation='relu'), tf.keras.layers.MaxPool2D(pool_size=(2,2)), tf.keras.layers.BatchNormalization(axis=-1), tf.keras.layers.Flatten(), tf.keras.layers.Dense(512,activation='relu'), tf.keras.layers.BatchNormalization() , tf.keras.layers.Dropout(rate=0.5), tf.keras.layers.Dense(3,activation='softmax') ]) learning_rate = 0.001 epochs=2 opt= tf.keras.optimizers.Adam(learning_rate=learning_rate , decay=learning_rate/(epochs*0.5)) model.compile(loss='sparse_categorical_crossentropy',optimizer=opt,metrics=['accuracy']) aug = tf.keras.preprocessing.image.ImageDataGenerator( rotation_range=10, zoom_range=0.15, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.15, horizontal_flip= False, vertical_flip= False, fill_mode="nearest" ) X_train = tf.random.normal((50, 256, 256, 3)) y_train = tf.random.uniform((50, ), maxval=3, dtype=tf.int32) history = model.fit(aug.flow(X_train, y_train, batch_size=2), epochs=epochs)
    Use the dummy data as an orientation for your real data.

Solution 1

You just have to make sure your labels are zero-based starting from 0 to 2, since your output layer has 3 nodes and a softmax activation function and you are using sparse_categorical_crossentropy. Here is a working example:

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(filters=16, kernel_size=(3,3), activation='relu',input_shape=(256, 256, 3)),
    tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu'),
    tf.keras.layers.MaxPool2D(pool_size=(2,2)),
    tf.keras.layers.BatchNormalization(axis=-1),

    tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu'),
    tf.keras.layers.Conv2D(filters=128, kernel_size=(3,3), activation='relu'),
    tf.keras.layers.MaxPool2D(pool_size=(2,2)),
    tf.keras.layers.BatchNormalization(axis=-1),

    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512,activation='relu'),
    tf.keras.layers.BatchNormalization() ,
    tf.keras.layers.Dropout(rate=0.5),

    tf.keras.layers.Dense(3,activation='softmax')

])

learning_rate = 0.001
epochs=2
opt= tf.keras.optimizers.Adam(learning_rate=learning_rate , decay=learning_rate/(epochs*0.5))
model.compile(loss='sparse_categorical_crossentropy',optimizer=opt,metrics=['accuracy'])


aug = tf.keras.preprocessing.image.ImageDataGenerator(
          rotation_range=10,
          zoom_range=0.15,
          width_shift_range=0.1,
          height_shift_range=0.1,
          shear_range=0.15,
          horizontal_flip= False,
          vertical_flip= False,
          fill_mode="nearest"
          )
          

X_train = tf.random.normal((50, 256, 256, 3))
y_train = tf.random.uniform((50, ), maxval=3, dtype=tf.int32)
history = model.fit(aug.flow(X_train, y_train, batch_size=2), epochs=epochs)

Use the dummy data as an orientation for your real data.

Original Author AloneTogether Of This Content

Solution 2

Otherwise if you use Colab. Please change run time type by selecting Runtime -> Change run time type -> GPU

Original Author N Tan Of This Content

Solution 3

The same issue happens with me so you need to make sure that they’re all of the same image aspect ratios (ex: 1:1, 4:3, 16:9, etc,.)

Original Author harsha Peteti Of This Content

Conclusion

So This is all About This Tutorial. Hope This Tutorial Helped You. Thank You.

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I am an Information Technology Engineer. I have Completed my MCA And I have 4 Year Plus Experience, I am a web developer with knowledge of multiple back-end platforms Like PHP, Node.js, Python and frontend JavaScript frameworks Like Angular, React, and Vue.

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