how to reshape input image array from 1d to 3d

I have built the image classifier as bellow: import tensorflow as tf from tensorflow.keras.applications.mobilenet import preprocess_input image_width, image_height = 224, 224 input_shape = (image_width, image_height, 3) self.model = tf.keras.Sequential() pretrained_layer = tf.keras.applications.mobilenet.MobileNet( weights=”imagenet”, include_top=False, input_shape=self.input_shape ) self.model.add(pretrained_layer) self.model.add(tf.keras.layers.GlobalAveragePooling2D()) self.model.add(tf.keras.layers.Dense(256, activation=”relu”)) self.model.add(tf.keras.layers.Dropout(0.5)) self.model.add(tf.keras.layers.Dense(128, activation=”relu”)) self.model.add(tf.keras.layers.Dropout(0.2)) self.model.add(tf.keras.layers.Dense(len(DATA_LABELS), activation=”sigmoid”)) self.model.compile( optimizer=tf.keras.optimizers.Adam(0.0005), loss=”binary_crossentropy”, metrics=[“accuracy”], ) I also had a…

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When rolling back a subvolume to a snapshot in btrfs, is simply renaming and keeping the old subvolume bad practice?

I’m new to btrfs. I’m experimenting with snapshots and rollbacks. I read the answers here: Conceptualizing btrfs – understanding snapshots, and space used btrfs confused about subvolumes Online remount btrfs of root filesystem with different subvolume (snapshot) I still don’t get it. My subvolid=0 is not mounted at boot. Instead, I have three subvolumes mounted…

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