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))
error:
dataset.__iter__() is only supported when eager execution is enabled.
I fixed it by enabling eager execution after importing tensorflow:
import tensorflow as tf
tf.enable_eager_execution()
转载
https://stackoverflow.com/questions/55576133/tensorflow-2-0-dataset-iter-is-only-supported-when-eager-execution-is-enab?noredirect=1&lq=1
