model.fit gives InvalidArgumentError: Graph execution error:

Question

My code is as follows:

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator
#import os
#os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
model = Sequential()
model.add(Conv2D(16, (3, 3), input_shape=(32,32, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))


model.add(Dropout(0.5))
model.add(Dense(27))
model.add(Activation('sigmoid'))

model.compile(loss = 'categorical_crossentropy',
              optimizer = 'rmsprop',
              metrics = ['accuracy'])

batch_size = 5

# Training Augmentation configuration

train_datagen = ImageDataGenerator(rescale = 1./255, 
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = False)

# Testing Augmentation - Only Rescaling
test_datagen = ImageDataGenerator(rescale = 1./255)

# Generates batches of Augmented Image data
train_generator = train_datagen.flow_from_directory('D:/college_project/resources/training/', 
                                                    target_size = (64, 64), 
                                                    batch_size = batch_size,
                                                    class_mode = 'categorical') 

# Generator for validation data
validation_generator = test_datagen.flow_from_directory('D:/college_project/resources/testing/', 
                                                        target_size = (64, 64),
                                                        batch_size = batch_size,
                                                        class_mode = 'categorical')

# Fit the model on Training data

model.fit(train_generator, epochs=5, validation_data=validation_generator)


# Evaluating model performance on Testing data
loss, accuracy = model.evaluate(validation_generator)

print("\nModel's Evaluation Metrics: ")
print("---------------------------")
print("Accuracy: {} \nLoss: {}".format(accuracy, loss))```

I am working on image classification but I am getting this error:

Traceback (most recent call last):

  File "D:\college_project\modules\traing example.py", line 56, in <module>
    `model.fit(train_generator, epochs=5, validation_data=validation_generator)`

  File "C:\Users\shubh\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None

  File "C:\Users\shubh\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py", line 54, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,

InvalidArgumentError: Graph execution error:

Answer 1

You need to flatten (make sure your output is 1D) your output after the last MaxPooling2D layer before feeding it to your output layer and since you are using categorical_crossentropy as your loss function, you should use a softmax activation function instead of sigmoid. Also, 27 nodes in your output layer means you have 27 different classes. Check if that is really the case. Here is a working example:

import tensorflow as tf
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator


flowers = tf.keras.utils.get_file(
    'flower_photos',
    'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',
    untar=True)

train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255, 
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = False)


train_generator = train_datagen.flow_from_directory(directory = flowers,
                                               batch_size = 32,
                                               target_size = (32, 32),
                                               seed = 42, class_mode='categorical')

model = Sequential()
model.add(Conv2D(16, (3, 3), input_shape=(32,32, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(5))
model.add(Activation('softmax'))

model.compile(loss = 'categorical_crossentropy',
              optimizer = 'rmsprop',
              metrics = ['accuracy'])

model.fit(train_generator, epochs=5)