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I got the same error, and I found that according to the TensorFlow docs, the option has to be keep_dims instead of keepdims in the latest version, and Tensorflow 1.4.1 also.
when running train.py i get the following issue:
C:\Users\cknau\Downloads\pixel-cnn-master\pixel-cnn-master>python train2.py
input args:
{
"data_dir":"D:\PixelCNN\dataset",
"save_dir":"D:\PixelCNN\samples",
"data_set":"cifar",
"save_interval":20,
"load_params":false,
"nr_resnet":5,
"nr_filters":160,
"nr_logistic_mix":10,
"resnet_nonlinearity":"concat_elu",
"class_conditional":false,
"energy_distance":false,
"learning_rate":0.001,
"lr_decay":0.999995,
"batch_size":16,
"init_batch_size":16,
"dropout_p":0.5,
"max_epochs":5000,
"nr_gpu":8,
"polyak_decay":0.9995,
"num_samples":1,
"seed":1
}
Traceback (most recent call last):
File "train2.py", line 120, in
loss_gen.append(loss_fun(tf.stop_gradient(xs[i]), out))
File "C:\Users\cknau\Downloads\pixel-cnn-master\pixel-cnn-master\pixel_cnn_pp\nn.py", line 83, in discretized_mix_logistic_loss
log_probs = tf.reduce_sum(log_probs,3) + log_prob_from_logits(logit_probs)
File "C:\Users\cknau\Downloads\pixel-cnn-master\pixel-cnn-master\pixel_cnn_pp\nn.py", line 27, in log_prob_from_logits
m = tf.reduce_max(x, axis, keepdims=True)
TypeError: reduce_max() got an unexpected keyword argument 'keepdims'
Anyone know what's going on here. I have NumPy 1.13, so that's not the issue.
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