randint ( 0, BATCH_SIZE - 1 ) random_image = test_images random_pred_mask = pred_masks random_true_mask = test_masks fig, ax = plt. argmax ( pred_masks, axis =- 1 ) pred_masks = pred_masks # Randomly select an image from the test batch random_index = random. epoch_interval = epoch_interval def on_epoch_end ( self, epoch, logs = None ): if self. Callback ): def _init_ ( self, epoch_interval = None ): self. Loss, and visually inspect the images, predicted masks and ground truth masks.Ĭlass Displa圜allback ( keras. We subclass Callback to monitor the model training progress: training and validation test_images, test_masks = next ( iter ( resized_val_ds )) Model ( inputs, outputs ) return model # Taking a batch of test inputs to measure model's progress. Conv2D ( num_classes, 3, activation = "softmax", padding = "same" )( x ) # Define the model model = keras. add () # Add back residual previous_block_activation = x # Set aside next residual # Add a per-pixel classification layer outputs = keras. Conv2D ( filters, 1, padding = "same" )( residual ) x = keras. UpSampling2D ( 2 )( previous_block_activation ) residual = keras. UpSampling2D ( 2 )( x ) # Project residual residual = keras. Conv2DTranspose ( filters, 3, padding = "same" )( x ) x = keras.
add () # Add back residual previous_block_activation = x # Set aside next residual # for filters in : x = keras. Conv2D ( filters, 1, strides = 2, padding = "same" )( previous_block_activation ) x = keras. MaxPooling2D ( 3, strides = 2, padding = "same" )( x ) # Project residual residual = keras. SeparableConv2D ( filters, 3, padding = "same" )( x ) x = keras. Activation ( "relu" )( x ) previous_block_activation = x # Set aside residual # Blocks 1, 2, 3 are identical apart from the feature depth. Conv2D ( 32, 3, strides = 2, padding = "same" )( inputs ) x = keras. Input ( shape = img_size + ( 3 ,)) # Entry block x = keras. Def get_model ( img_size, num_classes ): inputs = keras.
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