Keras slicing index out of range beginner question - tensorflow

Question

I am using Keras and Tensorflow for the first time. I am trying to get vgg16 to train on the imagenette dataset.
Im not sure why I am getting an index out of range error. Maybe I am doing something obviously wrong. Ive been messing with this for awhile now so any help would be great!
Attached is the error and source code
I resized my images to the correct size based on the vgg16 documentation: https://keras.io/api/applications/vgg/#vgg16-function
`
tfds_name = 'imagenette'
(ds_train, ds_validation), ds_info= tfds.load(
name=tfds_name,
split=['train', 'validation'],
with_info = True,
as_supervised=True)
#model from assignment link https://keras.io/api/applications/vgg/#vgg16-function
ourModel = tf.keras.applications.VGG16(
include_top=True, #3 fill layers on top
weights="imagenet", #use imagenet
input_tensor=None, #use another layer as input
input_shape=None, #inly set if include to false
pooling=None, #use with include top false
classes=1000, #number of classes to set, we use imagenet values
classifier_activation="softmax", # classifier on input can only be none or softmax on pretrained
)
#make it so layers frozen
#for layer in ourModel.layers[:-1]:
# layer.trainable = False
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy()
ourModel.compile(optimizer="adam",
loss=loss_fn,
metrics=['accuracy'])
def reshape(img,label):
img = tf.cast(img, tf.float32)
img = tf.image.resize(img, (224,224))
resize_image = tf.reshape(img, [-1, 224, 224, 3])
resize_image = preprocess_input(resize_image)
return resize_image, label
ds_train = ds_train.map(reshape)
ds_validation = ds_validation.map(reshape)
ourModel.fit(ds_train,
epochs=10,
validation_data = ds_validation)
`
Error:
ValueError: in user code:
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1051, in train_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1040, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1030, in run_step **
outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 890, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 949, in compute_loss
y, y_pred, sample_weight, regularization_losses=self.losses)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 212, in __call__
batch_dim = tf.shape(y_t)[0]
ValueError: slice index 0 of dimension 0 out of bounds. for '{{node strided_slice}} = StridedSlice[Index=DT_INT32, T=DT_INT32, begin_mask=0, ellipsis_mask=0, end_mask=0, new_axis_mask=0, shrink_axis_mask=1](Shape, strided_slice/stack, strided_slice/stack_1, strided_slice/stack_2)' with input shapes: [0], [1], [1], [1] and with computed input tensors: input[1] = <0>, input[2] = <1>, input[3] = <1>.

Answer 1

Your reshape function is creating a single batch for the image data but not for label data. So return resize_image, label[None, ...] should fix the above bug.
But the right way to batch the train (and valid dataset) is to do: ds_train=ds_train.batch(BATCH_SIZE) and then removing tf.reshape(img, [-1, 224, 224, 3])