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there is not python3.4 with keras2.1.4 and tensorflow1.0.1 ; so , i install python3.5 with keras2.1.4 and tensorflow1.0.1
See original GitHub issuethere is not python3.4 with keras2.1.4 and tensorflow1.0.1 ; so , i install python3.5 with keras2.1.4 and tensorflow1.0.1
@TitanX:~$ conda search tensorflow
Loading channels: done
Name Version Build Channel
tensorflow 0.10.0rc0 np111py27_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
tensorflow 0.10.0rc0 np111py27_0 https://mirrors.ustc.edu.cn/anaconda/pkgs/free
tensorflow 0.10.0rc0 np111py27_0 defaults
tensorflow 0.10.0rc0 np111py34_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
tensorflow 0.10.0rc0 np111py34_0 https://mirrors.ustc.edu.cn/anaconda/pkgs/free
tensorflow 0.10.0rc0 np111py34_0 defaults
tensorflow 0.10.0rc0 np111py35_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
tensorflow 0.10.0rc0 np111py35_0 https://mirrors.ustc.edu.cn/anaconda/pkgs/free
tensorflow 0.10.0rc0 np111py35_0 defaults
tensorflow 1.0.1 np112py27_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
tensorflow 1.0.1 np112py27_0 https://mirrors.ustc.edu.cn/anaconda/pkgs/free
tensorflow 1.0.1 np112py27_0 defaults
tensorflow 1.0.1 np112py35_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
tensorflow 1.0.1 np112py35_0 https://mirrors.ustc.edu.cn/anaconda/pkgs/free
tensorflow 1.0.1 np112py35_0 defaults
tensorflow 1.0.1 np112py36_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
tensorflow 1.0.1 np112py36_0 https://mirrors.ustc.edu.cn/anaconda/pkgs/free
tensorflow 1.0.1 np112py36_0 defaults
tensorflow 1.1.0 np111py27_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
tensorflow 1.1.0 np111py27_0 https://mirrors.ustc.edu.cn/anaconda/pkgs/free
keras 2.1.2 py35_0 defaults
keras 2.1.2 py36_0 defaults
keras 2.1.3 py27_0 defaults
keras 2.1.3 py35_0 defaults
keras 2.1.3 py36_0 defaults
keras 2.1.4 py27_0 defaults
keras 2.1.4 py35_0 defaults
keras 2.1.4 py36_0 defaults
keras 2.1.5 py27_0 defaults
there is not python3.4 with keras2.1.4 and tensorflow1.0.1 ; so , i install python3.5 with keras2.1.4 and tensorflow1.0.1 . Then, happen follow error:
/home/anaconda2/envs/dronet/bin/python -u /home/pytest/dronet/rpg_public_dronet-master1/cnn.py --experiment_rootdir='./model/test_1' --train_dir='/home/datafile/dronet_data/collision_dataset/training' --val_dir='/home/datafile/dronet_data/collision_dataset/validation' --batch_size=16 --epochs=150 --log_rate=25
Using TensorFlow backend.
Found 63169 images belonging to 132 experiments.
Found 1035 images belonging to 3 experiments.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 200, 200, 1) 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 100, 100, 32) 832 input_1[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 49, 49, 32) 0 conv2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 49, 49, 32) 128 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 49, 49, 32) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 25, 25, 32) 9248 activation_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 25, 25, 32) 128 conv2d_2[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 25, 25, 32) 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 25, 25, 32) 1056 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 25, 25, 32) 9248 activation_2[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, 25, 25, 32) 0 conv2d_4[0][0]
conv2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 25, 25, 32) 128 add_1[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 25, 25, 32) 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 13, 13, 64) 18496 activation_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 13, 13, 64) 256 conv2d_5[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 13, 13, 64) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 13, 13, 64) 2112 add_1[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 13, 13, 64) 36928 activation_4[0][0]
__________________________________________________________________________________________________
add_2 (Add) (None, 13, 13, 64) 0 conv2d_7[0][0]
conv2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 13, 13, 64) 256 add_2[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, 13, 13, 64) 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 7, 7, 128) 73856 activation_5[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 7, 7, 128) 512 conv2d_8[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, 7, 7, 128) 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 7, 7, 128) 8320 add_2[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 7, 7, 128) 147584 activation_6[0][0]
__________________________________________________________________________________________________
add_3 (Add) (None, 7, 7, 128) 0 conv2d_10[0][0]
conv2d_9[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 6272) 0 add_3[0][0]
__________________________________________________________________________________________________
activation_7 (Activation) (None, 6272) 0 flatten_1[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 6272) 0 activation_7[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 1) 6273 dropout_1[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 1) 6273 dropout_1[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, 1) 0 dense_2[0][0]
==================================================================================================
Total params: 321,634
Trainable params: 320,930
Non-trainable params: 704
__________________________________________________________________________________________________
None
configure_output_dir: not storing the git diff, probably because you're not in a git repo
Logging data to ./model/test_1/log.txt
/home/anaconda2/envs/dronet/lib/python3.5/site-packages/tensorflow/python/ops/gradients_impl.py:91: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
"Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
Epoch 1/150
1.0
0.0
Traceback (most recent call last):
File "/home/anaconda2/envs/dronet/lib/python3.5/site-packages/keras/utils/data_utils.py", line 564, in get
inputs = self.queue.get(block=True).get()
File "/home/anaconda2/envs/dronet/lib/python3.5/multiprocessing/pool.py", line 644, in get
raise self._value
File "/home/anaconda2/envs/dronet/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/home/anaconda2/envs/dronet/lib/python3.5/site-packages/keras/utils/data_utils.py", line 390, in get_index
return _SHARED_SEQUENCES[uid][i]
File "/home/anaconda2/envs/dronet/lib/python3.5/site-packages/keras/preprocessing/image.py", line 799, in __getitem__
return self._get_batches_of_transformed_samples(index_array)
File "/home/anaconda2/envs/dronet/lib/python3.5/site-packages/keras/preprocessing/image.py", line 845, in _get_batches_of_transformed_samples
raise NotImplementedError
NotImplementedError
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/pytest/dronet/rpg_public_dronet-master1/cnn.py", line 176, in <module>
main(sys.argv)
File "/home/pytest/dronet/rpg_public_dronet-master1/cnn.py", line 172, in main
_main()
File "/home/pytest/dronet/rpg_public_dronet-master1/cnn.py", line 161, in _main
trainModel(train_generator, val_generator, model, initial_epoch)
File "/home/pytest/dronet/rpg_public_dronet-master1/cnn.py", line 89, in trainModel
initial_epoch=initial_epoch)
File "/home/anaconda2/envs/dronet/lib/python3.5/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "/home/anaconda2/envs/dronet/lib/python3.5/site-packages/keras/engine/training.py", line 2212, in fit_generator
generator_output = next(output_generator)
File "/home/anaconda2/envs/dronet/lib/python3.5/site-packages/keras/utils/data_utils.py", line 570, in get
six.raise_from(StopIteration(e), e)
File "<string>", line 2, in raise_from
StopIteration
Issue Analytics
- State:
- Created 5 years ago
- Comments:11
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I used the versions fixed before (python 3.4 / keras 2.1.4 / Tensorflow 1.5.0). I substituted the next function with the 2 functions above. In the trainModel function in cnn.py, “decay” is not recognized as argument of the compile function despite the fact that is a learning rate for the optimizer used to compile the model. So i written it like this optimizer=optimizers.Adam(decay=1e-5) model.compile(loss=[utils.hard_mining_mse(model.k_mse), utils.hard_mining_entropy(model.k_entropy)], optimizer=optimizer, loss_weights=[model.alpha, model.beta])
@MerouaneB thanks for your feedback. I will soon update the repo to adjust for the new changes in Keras.