NameError: name 'prefetch' is not defined

[Killpit]

I am currently working with malaria dataset and when I try to use prefetch, I get the error mentioned above.

Here is the source code:

-- coding: utf-8 --

"""malaria_detection.ipynb

Automatically generated by Colaboratory.

Original file is located at
https://colab.research.google.com/drive/1bRu3Nej0EPd9aofiB4vFsJZNgpyWAs2O

Importing Libraries

"""

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import tensorflow_datasets as tfds
from keras.layers import Conv2D, MaxPool2D, Dense, InputLayer, Flatten
from keras import Sequential
from keras.optimizers import Adam
from keras.losses import BinaryCrossentropy

"""# Data Preparation

Data Loading

"""

dataset, dataset_info = tfds.load('malaria', with_info = True, as_supervised = True, shuffle_files = True, split = ['train'])

for data in dataset[0].take(4):
print(data)

dataset

dataset_info

def splits(dataset, TRAIN_RATIO, VAL_RATIO, TEST_RATIO):

DATASET_SIZE = len(dataset)

train_dataset = dataset.take(int(TRAIN_RATIO*DATASET_SIZE))

val_test_dataset = dataset.skip(int(TRAIN_RATIODATASET_SIZE))
val_dataset = val_test_dataset.take(int(VAL_RATIODATASET_SIZE))

test_dataset = val_test_dataset.skip(int(VAL_RATIO*DATASET_SIZE))
return train_dataset, val_dataset, test_dataset

TRAIN_RATIO = 0.8
VAL_RATIO = 0.1
TEST_RATIO = 0.1

#dataset = tf.data.Dataset.range(10)
train_dataset, val_dataset, test_dataset = splits(dataset[0], TRAIN_RATIO, VAL_RATIO, TEST_RATIO)
print(list(train_dataset.take(1).as_numpy_iterator()), list(val_dataset.take(1).as_numpy_iterator()), list(test_dataset.take(1).as_numpy_iterator()))

dataset

"""# Data Visualization"""

for i, (image, label) in enumerate(train_dataset.take(16)):
ax = plt.subplot(4, 4, i + 1)
plt.imshow(image)
plt.title(dataset_info.features['label'].int2str(label))
plt.axis('off')

dataset_info.features['label'].int2str(0)

"""# Data Preprocessing"""

IM_SIZE = 224

def resize_rescale(image, label):
return tf.image.resize(image, (IM_SIZE, IM_SIZE))/255.0, label

train_dataset = train_dataset.map(resize_rescale)
val_dataset = val_dataset.map(resize_rescale)
test_dataset = test_dataset.map(resize_rescale)

train_dataset
val_dataset
test_dataset

for image, label in train_dataset.take(1):
print(image, label)

BATCH_SIZE = 32
train_dataset = train_dataset.shuffle(buffer_size = 8, reshuffle_each_iteration= True).batch(BATCH_SIZE), prefetch(tf.data.AUTOTUNE)

val_dataset = val_dataset.shuffle(buffer_size = 8, reshuffle_each_iteration= True).batch(BATCH_SIZE), prefetch(tf.data.AUTOTUNE)

train_dataset

"""# Model Creation and Training"""

lenet_model = Sequential([
InputLayer(input_shape=(IM_SIZE, IM_SIZE, 3)),
Conv2D(filters=6, kernel_size=5, strides=1, padding='valid', activation='sigmoid'),
MaxPool2D(pool_size=2, strides=2),
Conv2D(filters=16, kernel_size=5, strides=1, padding='valid', activation='sigmoid'),
MaxPool2D(pool_size=2, strides=2),
Flatten(),
Dense(100, activation="sigmoid"),
Dense(10, activation="sigmoid"),
Dense(1, activation="sigmoid"),
])

lenet_model.summary()

y_true = [0, 1, 0, 0]
y_pred = [0.6, 0.51, 0.94, 1]
bce = tf.keras.losses.BinaryCrossentropy()
bce(y_true, y_pred)

lenet_model.compile(optimizer=Adam(learning_rate=0.01), loss=BinaryCrossentropy())

history = lenet_model.fit(train_dataset, validation_data=val_dataset, epochs=100, verbose=1)

train_dataset

val_dataset

[Killpit]

I solved the mistake, I simply had to remove "," and add ".". Sorry for causing a misunderstanding, I'll let you know if something more serious comes up.