I did a neural network machine learning on colored images (3 channels). It worked but now I want to try to do it in grayscale to see if I can improve accuracy. Here is the code:
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( train_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode='binary', shuffle=True) validation_generator = test_datagen.flow_from_directory( validation_data_dir, target_size=(img_width, img_height), batch_size=batch_size, color_mode='grayscale', class_mode='binary', shuffle=True) model = tf.keras.Sequential() input_shape = (img_width, img_height, 1) model.add(Conv2D(32, 2, input_shape=input_shape, activation='relu')) model.add(MaxPooling2D(pool_size=2)) model.add(Conv2D(32, 2, activation='relu')) model.add(MaxPooling2D(pool_size=2)) model.add(Conv2D(64, 2, activation='relu')) model.add(MaxPooling2D(pool_size=2)) model.add(Flatten()) model.add(Dense(128)) model.add(Dense(len(classes))) model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) history = model.fit(train_generator, validation_data=validation_generator, epochs=EPOCHS)
You can see that I have changed the input_shape to have 1 single channel for grayscale. I’m getting an error:
Node: 'sequential_26/conv2d_68/Relu' Fused conv implementation does not support grouped convolutions for now. [[{{node sequential_26/conv2d_68/Relu}}]] [Op:__inference_train_function_48830]
Any idea how to fix this?
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Answer
Your train_generator
does not seem to have the colormode='grayscale'
. Try:
train_generator = train_datagen.flow_from_directory( train_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode='binary', colormode='grayscale', shuffle=True)