MNIST Data set up batch - python-3.x

Question

I am at the step of train the model. However, when I apply the code from a tutorial: batch_x, batch_y = mnist.train.next_batch(50). It shows that there is no attribute 'train' in the TensorFlow model. I know it is outdated code, and I tried to convert to new version of TensorFlow. However, i couldn't find a matching code that can do the same thing as the above line of code do. I bet there is a way but I couldn't come up with one solution.
I found a method that asked me to use tf.data.Dataset.batch(batch_size).
I tried the following way, but none of them works.
a. batch_x, batch_y = mnist.train.next_batch(50)
b. batch_x, batch_y = tf.data.Dataset.batch(batch_size)
c. batch_x, batch_y = tf.data.Dataset.batch(50)
d. batch_x, batch_y = mnist.batch(50)
with tf.Session() as sess:
#FIrst, run vars_initializer to initialize all variables
sess.run(vars_initializer)
for i in range(steps):
#Each batch: 50 images
batch_x, batch_y = mnist.train.next_batch(50)
#Train the model
#Dropout keep_prob (% to keep): 0.5 --> 50% will be dropped out
sess.run(cnn_trainer, feed_dict={x: batch_x, y_true: batch_y, hold_prob: 0.5})
#Test the model: at each 100th step
#Run this block of code for each 100 times of training, each time run a batch
if i % 100 == 0:
print('ON STEP: {}'.format(i))
print('ACCURACY: ')
#Compare to find matches of y_pred and y_true
matches = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y_true, 1))
#Cast the matches from integers to tf.float32
#Calculate the accuracy using the mean of matches
acc = tf.reduce_mean(tf.cast(matches, tf.float32))
#Test the model at each 100th step
#Using test dataset
#Dropout: NONE because of test, not training.
test_accuracy = sess.run(acc, feed_dict = {x:mnist.test.images, y_true:mnist.test.labels, hold_prob:1.0})
print(test_accuracy)
print('\n')

Answer 1

You can use tf.keras.datasets.mnist.load_data. It returns a tuple of Numpy arrays: (x_train, y_train), (x_test, y_test).
After that, you need to create dataset object using Dataset API. This will create training dataset. Test dataset could be created in the same fashion.
train, test = tf.keras.datasets.mnist.load_data()
dataset = tf.data.Dataset.from_tensor_slices((train[0], train[1]))
Then, to create batch, you need to apply batch function to it
dataset = dataset.batch(1)
To output it contents or use it in training you need to create iterator. Code below creates most common iterator and outputs element of batch_size in this case 1.
iterator = dataset.make_one_shot_iterator()
with tf.Session() as sess:
print(sess.run(iterator.get_next())
Please read https://www.tensorflow.org/guide/datasets

Answer 2

This uses TensorFlow 1.11.0 and Keras and intended to show how to use the batch. You have to adapt it to your need.
import tensorflow as tf
from tensorflow import keras as k
(x_train, y_train), (X_test, Y_test) = tf.keras.datasets.mnist.load_data()
X_train = x_train.reshape(x_train.shape[0], 28, 28,1)
y_train = tf.keras.utils.to_categorical(y_train,10)
X_test = X_test.reshape(X_test.shape[0], 28, 28,1)
Y_test = tf.keras.utils.to_categorical(Y_test,10)
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
train_dataset = train_dataset.batch(32)
test_dataset = tf.data.Dataset.from_tensor_slices((X_test, Y_test))
test_dataset = test_dataset.batch(32)
model = tf.keras.models.Sequential([
tf.keras.layers.Convolution2D(32, (2, 2), activation='relu', input_shape=(28, 28,1)),
tf.keras.layers.MaxPool2D(pool_size=2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(10, activation='softmax')
])
tbCallback = [
k.callbacks.TensorBoard(
log_dir="D:/TensorBoard", histogram_freq=1, write_graph=True, write_images=True
)
]
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_dataset, epochs = 10, steps_per_epoch = 30,validation_data=test_dataset,validation_steps=1, callbacks=tbCallback)