TensorFlow 2.0 dataset.__iter__() is only supported when eager execution is enabled

Question

I'm using the following custom training code in TensorFlow 2:

def parse_function(filename, filename2):
    image = read_image(fn)
    def ret1(): return image, read_image(fn2), 0
    def ret2(): return image, preprocess(image), 1
    return tf.case({tf.less(tf.random.uniform([1])[0], tf.constant(0.5)): ret2}, default=ret1)

dataset = tf.data.Dataset.from_tensor_slices((train,shuffled_train))
dataset = dataset.shuffle(len(train))
dataset = dataset.map(parse_function, num_parallel_calls=4)
dataset = dataset.batch(1)
dataset = dataset.prefetch(buffer_size=4)

@tf.function
def train(model, dataset, optimizer):
    for x1, x2, y in enumerate(dataset):
        with tf.GradientTape() as tape:
            left, right = model([x1, x2])
            loss = contrastive_loss(left, right, tf.cast(y, tf.float32))
        gradients = tape.gradient(loss, model.trainable_variables)
        optimizer.apply_gradients(zip(gradients, model.trainable_variables))

siamese_net.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=1e-3))
train(siamese_net, dataset, tf.keras.optimizers.RMSprop(learning_rate=1e-3))

This code gives me the error:

dataset.__iter__() is only supported when eager execution is enabled.

However, it's in TensorFlow 2.0 so eager is enabled by default. tf.executing_eagerly() also returns 'True'.

Answer 1

I fixed it by enabling eager execution after importing tensorflow:

import tensorflow as tf

tf.enable_eager_execution()

Reference: Tensorflow

Answer 2

In case you are using Jupyter notebook after

import tensorflow as tf

tf.enable_eager_execution()

You need to restart the kernel and it works