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MWE copy tensor bug
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import numpy as np | |
import os | |
import tensorflow as tf | |
from tensorflow.keras.layers import Dense, Flatten | |
from tensorflow.keras.optimizers import Adam | |
os.environ['CUDA_VISIBLE_DEVICES'] = '0' | |
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true' | |
def get_node(idx): | |
idx = idx + 1 | |
return idx * np.ones((100, 100, 3)), 1 | |
def get_node_pyfunc(idx): | |
tensors = tf.py_function( | |
func=get_node, | |
inp=[idx], | |
Tout=(tf.int64, tf.int64) | |
) | |
tensors[0].set_shape(tf.TensorShape([None, None, None])) | |
tensors[1].set_shape(tf.TensorShape([])) | |
return tensors | |
dataset = tf.data.Dataset.range(1000) | |
dataset = dataset.repeat(20) | |
dataset = dataset.map(get_node_pyfunc, num_parallel_calls=4) | |
dataset = dataset.batch(batch_size=20) | |
model = tf.keras.Sequential() | |
model.add(Flatten(input_shape=(100, 100, 3))) | |
model.add(Dense(1000)) | |
model.add(Dense(1000)) | |
model.add(Dense(1)) | |
model.compile(optimizer=Adam(), loss='binary_crossentropy') | |
model.fit(dataset, verbose=True, steps_per_epoch=1000 / 20, epochs=20) |
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