CODEWITHSUNDEEP
pythontensorflowkerastf.keras
Noah Barrett
Noah Barrett

Reputation: 3

How to Pre-process image for keras.VGG19?

I am attempting to train the keras VGG-19 model on RGB images, when attempting to feed forward this error arises:

ValueError: Input 0 of layer block1_conv1 is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: [224, 224, 3]

When reshaping image to (224, 224, 3, 1) to include batch dim, and then feeding forward as shown in code, this error occurs:

ValueError: Dimensions must be equal, but are 1 and 3 for '{{node BiasAdd}} = BiasAdd[T=DT_FLOAT, data_format="NHWC"](strided_slice, Const)' with input shapes: [64,224,224,1], [3]

for idx in tqdm(range(train_data.get_ds_size() // batch_size)):
    # train step
    batch = train_data.get_train_batch()
    for sample, label in zip(batch[0], batch[1]):
        sample = tf.reshape(sample, [*sample.shape, 1])
        label = tf.reshape(label, [*label.shape, 1])
        train_step(idx, sample, label)

vgg is intialized as:

vgg = tf.keras.applications.VGG19(
                            include_top=True,
                            weights=None,
                            input_tensor=None,
                            input_shape=[224, 224, 3],
                            pooling=None,
                            classes=1000,
                            classifier_activation="softmax"
                        )

training function:

@tf.function
def train_step(idx, sample, label):
  with tf.GradientTape() as tape:
    # preprocess for vgg-19
    sample = tf.image.resize(sample, (224, 224))
    sample = tf.keras.applications.vgg19.preprocess_input(sample * 255)

    predictions = vgg(sample, training=True)
    # mean squared error in prediction
    loss = tf.keras.losses.MSE(label, predictions)

  # apply gradients
  gradients = tape.gradient(loss, vgg.trainable_variables)
  optimizer.apply_gradients(zip(gradients, vgg.trainable_variables))

  # update metrics
  train_loss(loss)
  train_accuracy(vgg, predictions)

I am wondering how the input should be formatted such that the keras VGG-19 implementation will accept it?

Upvotes: 0

Views: 823

Answers (2)

Nicolas Gervais
Nicolas Gervais

Reputation: 36594

You will have to unsqueeze one dimension to turn your shape into [1, 224, 224, 3':

for idx in tqdm(range(train_data.get_ds_size() // batch_size)):
    # train step
    batch = train_data.get_train_batch()
    for sample, label in zip(batch[0], batch[1]):
        sample = tf.reshape(sample, [1, *sample.shape])  # added the 1 here
        label = tf.reshape(label, [*label.shape, 1])
        train_step(idx, sample, label)

Upvotes: 1

lenik
lenik

Reputation: 23498

You use wrong dimension for the image batch, "When reshaping image to (224, 224, 3, 1) to include batch dim" -- this should be (x, 224, 224, 3), where x is the number of the images in the batch.

Upvotes: 0

Related Questions