How to feed tfrecord file in a model and train?-CodePudding

I wrote a tfrecord file and fed in my Unet model but got a problem with the input shape. Below is my code.

About the data:

484 training images, each has a shape of (240, 240, 155, 4), these 4 numbers are the height, width, number of layers and channels respectively.
484 labels, each has a shape of (240, 240, 155)

I used the first 2 examples:

test_writer = tf.io.TFRecordWriter('test.tfrecords')

for i in range(2):
  example = create_example(image_paths[i], label_paths[i])
  test_writer.write(example.SerializeToString())

test_writer.close()

serialised_example = tf.data.TFRecordDataset('test.tfrecords')
parsed_example = serialised_example.map(parse_tfrecord)

My model architecture (I simplified it):

from tensorflow.keras.layers import Conv3D, Conv3DTranspose, Input, Rescaling

num_classes = 4

my_model = tf.keras.Sequential([

Input(shape = (240, 240, 155, 4)),
Rescaling(scale = 1./255),

Conv3D(filters = 64, kernel_size = 3, strides = 2, activation = 'relu', padding = 'same'),
Conv3D(filters = 64, kernel_size = 3, activation = 'relu', padding = 'same'),
# and more layers between...
Conv3DTranspose(filters = 64, kernel_size = 3, activation = 'relu', padding = 'same'),
Conv3DTranspose(filters = 64, kernel_size = 3, strides = 2, activation = 'relu', padding = 'same'),

Conv3D(filters = num_classes, kernel_size = 3, activation = 'softmax', padding = 'same')

])

my_model.compile(optimizer = 'rmsprop', loss = 'sparse_categorical_crossentropy')

I got my data set from my tfrecord file like this:

def get_image_and_label(features):
  image, label = features['image'], features['label']
  return image, label

def get_dataset(tfrecord_names):

  dataset = (tf.data.TFRecordDataset(tfrecord_names)
             .map(parse_tfrecord)
             .map(get_image_and_label))

  return dataset

dataset = get_dataset('test.tfrecords')

I started to train:

my_model.fit(dataset, epochs = 1)

And got this error: Input 0 of layer "sequential_2" is incompatible with the layer: expected shape=(None, 240, 240, 155, 4), found shape=(240, 240, 155, 4)

How can I fix this? Please tell me if you need more information (data link or my previous code).

CodePudding user response：

Your model expects the shape (samples, 240, 240, 155, 4), so try something like this:

dataset = get_dataset('test.tfrecords').batch(1)

And you have to set strides=1 if you want your labels to match your output:

from tensorflow.keras.layers import Conv3D, Conv3DTranspose, Input, Rescaling

num_classes = 4

my_model = tf.keras.Sequential([

Input(shape = (240, 240, 155, 4)),
Rescaling(scale = 1./255),
Conv3D(filters = 64, kernel_size = 3, strides = 1, activation = 'relu', padding = 'same'),
Conv3D(filters = 64, kernel_size = 3, activation = 'relu', padding = 'same'),
# and more layers between...
Conv3DTranspose(filters = 64, kernel_size = 3, activation = 'relu', padding = 'same'),
Conv3DTranspose(filters = 64, kernel_size = 3, strides = 1, activation = 'relu', padding = 'same'),

Conv3D(filters = num_classes, kernel_size = 3, activation = 'softmax', padding = 'same')
])