I’m trying to run a custom pykeras model similar to the tutorial here: https://github.com/lmoneta/tmva-tutorial/blob/master/notebooks/TMVA_CNN_Classification_py.ipynb
When the training is called on the methods
factory.TrainAllMethods()
After the completion of all epochs, I hit this error:
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
reshape_2 (Reshape) (None, 1, 32, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 6, 28, 28) 156
_________________________________________________________________
activation_6 (Activation) (None, 6, 28, 28) 0
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 6, 14, 14) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 16, 10, 10) 2416
_________________________________________________________________
activation_7 (Activation) (None, 16, 10, 10) 0
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 16, 5, 5) 0
_________________________________________________________________
flatten_2 (Flatten) (None, 400) 0
_________________________________________________________________
dense_4 (Dense) (None, 120) 48120
_________________________________________________________________
activation_8 (Activation) (None, 120) 0
_________________________________________________________________
dense_5 (Dense) (None, 84) 10164
_________________________________________________________________
activation_9 (Activation) (None, 84) 0
_________________________________________________________________
dense_6 (Dense) (None, 2) 170
_________________________________________________________________
activation_10 (Activation) (None, 2) 0
=================================================================
Total params: 61,026
Trainable params: 61,026
Non-trainable params: 0
_________________________________________________________________
Train on 12800 samples, validate on 3200 samples
Epoch 1/10
12800/12800 [==============================] - 3s 250us/step - loss: 0.1691 - accuracy: 0.9316 - val_loss: 0.1962 - val_accuracy: 0.9169
Epoch 00001: val_loss improved from inf to 0.19617, saving model to trained_model_cnn.h5
Epoch 2/10
12800/12800 [==============================] - 3s 231us/step - loss: 0.2114 - accuracy: 0.9129 - val_loss: 0.1964 - val_accuracy: 0.9181
Epoch 00002: val_loss did not improve from 0.19617
Epoch 3/10
12800/12800 [==============================] - 3s 242us/step - loss: 0.1637 - accuracy: 0.9341 - val_loss: 0.1147 - val_accuracy: 0.9581
Epoch 00003: val_loss improved from 0.19617 to 0.11468, saving model to trained_model_cnn.h5
Epoch 4/10
12800/12800 [==============================] - 3s 239us/step - loss: 0.1260 - accuracy: 0.9522 - val_loss: 0.2123 - val_accuracy: 0.9141
Epoch 00004: val_loss did not improve from 0.11468
Epoch 5/10
12800/12800 [==============================] - 3s 243us/step - loss: 0.1569 - accuracy: 0.9366 - val_loss: 0.1028 - val_accuracy: 0.9622
Epoch 00005: val_loss improved from 0.11468 to 0.10278, saving model to trained_model_cnn.h5
Epoch 6/10
12800/12800 [==============================] - 3s 245us/step - loss: 0.1121 - accuracy: 0.9590 - val_loss: 0.0921 - val_accuracy: 0.9659
Epoch 00006: val_loss improved from 0.10278 to 0.09208, saving model to trained_model_cnn.h5
Epoch 7/10
12800/12800 [==============================] - 3s 256us/step - loss: 0.1077 - accuracy: 0.9581 - val_loss: 0.0944 - val_accuracy: 0.9638
Epoch 00007: val_loss did not improve from 0.09208
Epoch 8/10
12800/12800 [==============================] - 3s 266us/step - loss: 0.1572 - accuracy: 0.9387 - val_loss: 0.1088 - val_accuracy: 0.9566
Epoch 00008: val_loss did not improve from 0.09208
Epoch 9/10
12800/12800 [==============================] - 3s 264us/step - loss: 0.0757 - accuracy: 0.9710 - val_loss: 0.1003 - val_accuracy: 0.9622
Epoch 00009: val_loss did not improve from 0.09208
Epoch 10/10
12800/12800 [==============================] - 3s 268us/step - loss: 0.0815 - accuracy: 0.9691 - val_loss: 0.0712 - val_accuracy: 0.9747
Epoch 00010: val_loss improved from 0.09208 to 0.07119, saving model to trained_model_cnn.h5
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<string> in <module>
TypeError: 'dict_keys' object is not subscriptable
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-9-4bb1800441f7> in <module>
----> 1 factory.TrainAllMethods()
Exception: void TMVA::Factory::TrainAllMethods() =>
FATAL error (C++ exception of type runtime_error)
Factory : Train all methods
Factory : Train method: PyKeras for Classification
:
: Failed to run python code
: ================================================================
: H e l p f o r M V A m e t h o d [ PyKeras ] :
:
: Keras is a high-level API for the Theano and Tensorflow packages.
: This method wraps the training and predictions steps of the Keras
: Python package for TMVA, so that dataloading, preprocessing and
: evaluation can be done within the TMVA system. To use this Keras
: interface, you have to generate a model with Keras first. Then,
: this model can be loaded and trained in TMVA.
:
:
: <Suppress this message by specifying "!H" in the booking option>
: ================================================================
:
: Split TMVA training data in 12800 training events and 3200 validation events
: Training Model Summary
: Option SaveBestOnly: Only model weights with smallest validation loss will be stored
Getting his:0
<WARNING> : Failed to run python code: copy_string=history.history.keys()[0]
<WARNING> : Python error message:
<FATAL> : Failed to run python code
***> abort program execution
How can I resolve this issue?
Thanks 