Getting No such file or directory [[{{node ReadFile}}]] [[IteratorGetNext]] [Op:__inference_train_function_9137] error - file

Question

This may be a simple answer, but currently making a neural network using keras and I ran into this problem through this code
\`EPOCHS = 50
callbacks = \[
tf.keras.callbacks.ReduceLROnPlateau(
monitor='val_loss', factor=0.1, patience=10, verbose=1, mode='min', min_delta=0.0001),
tf.keras.callbacks.ModelCheckpoint(
'weights.tf', monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=True),
tf.keras.callbacks.EarlyStopping(
monitor='val_loss', min_delta=0, patience=15, verbose=1, restore_best_weights=True)
\]
history = model.fit(
train_ds,
validation_data=val_ds,
verbose=1,
callbacks=callbacks,
epochs=EPOCHS,
)
model.load_weights('weights.tf')
model.evaluate(val_ds)\`
Output:
`Epoch 1/50
NotFoundError Traceback (most recent call last)
\<ipython-input-15-265d39d703c7\> in \<module\>
10 \]
11
\---\> 12 history = model.fit(
13 train_ds,
14 validation_data=val_ds,
1 frames
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
52 try:
53 ctx.ensure_initialized()
\---\> 54 tensors = pywrap_tfe.TFE_Py_Execute(ctx.\_handle, device_name, op_name,
55 inputs, attrs, num_outputs)
56 except core.\_NotOkStatusException as e:
NotFoundError: Graph execution error:
train/60377.jpg; No such file or directory
\[\[{{node ReadFile}}\]\]
\[\[IteratorGetNext\]\] \[Op:\__inference_train_function_9137\]
`
Here's my data:
FairFace Dataset from Kaggle
Here's how I preprocessed (through code I borrowed) the images from the FairFace dataset.
\`IMG_SIZE = 224
AUTOTUNE = tf.data.AUTOTUNE
BATCH_SIZE = 224
NUM_CLASSES = len(labels_map)
# Dataset creation
y_train = tf.keras.utils.to_categorical(train.race, num_classes=NUM_CLASSES, dtype='float32')
y_val = tf.keras.utils.to_categorical(val.race, num_classes=NUM_CLASSES, dtype='float32')
train_ds = tf.data.Dataset.from_tensor_slices((train.file, y_train)).shuffle(len(y_train))
val_ds = tf.data.Dataset.from_tensor_slices((val.file, y_val))
assert len(train_ds) == len(train.file) == len(train.race)
assert len(val_ds) == len(val.file) == len(val.race)
# Read files
def map_fn(path, label):
image = tf.io.decode_jpeg(tf.io.read_file(path))
image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))
return image, label
# Read files
train_ds = train_ds.map(lambda path, lbl: (tf.io.decode_jpeg(tf.io.read_file(path)), lbl), num_parallel_calls=AUTOTUNE)
val_ds = val_ds.map(lambda path, lbl: (tf.io.decode_jpeg(tf.io.read_file(path)), lbl), num_parallel_calls=AUTOTUNE)
# Batch and resize after batch, then prefetch
train_ds = val_ds.map(lambda imgs, lbls: (tf.image.resize(imgs, (IMG_SIZE, IMG_SIZE)), lbls), num_parallel_calls=AUTOTUNE)
val_ds = val_ds.map(lambda imgs, lbls: (tf.image.resize(imgs, (IMG_SIZE, IMG_SIZE)), lbls), num_parallel_calls=AUTOTUNE)
train_ds = train_ds.batch(BATCH_SIZE)
val_ds = val_ds.batch(BATCH_SIZE)
# Performance enchancement - cache, batch, prefetch
train_ds = train_ds.prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.prefetch(buffer_size=AUTOTUNE)\`
I tried changing the jpg file name but to no avail.