Thierry Herrmann thierryherrmann
thierryherrmann
/ save_untrained_and_reload.py
Created
August 2, 2020 23:48
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| # instantiate a new module and save it untrained | |
| module = CustomModule() | |
| save_module(module, model_dir) | |
| del module | |
| print('\n\n========== Reload module ===========') | |
| # the following works also if we reload in another python process | |
| model_dir = 'saved_model' |
thierryherrmann
/ saved_model_cli
Created
August 2, 2020 23:46
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| signature_def['my_serve']: | |
| The given SavedModel SignatureDef contains the following input(s): | |
| inputs['X'] tensor_info: | |
| dtype: DT_FLOAT | |
| shape: (-1, 8) | |
| name: my_serve_X:0 | |
| The given SavedModel SignatureDef contains the following output(s): | |
| outputs['output_0'] tensor_info: | |
| dtype: DT_FLOAT | |
| shape: (-1, 1) |
thierryherrmann
/ ckpt_out_2
Created
August 2, 2020 23:42
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| tensor : model/layer_with_weights-0/bias/.OPTIMIZER_SLOT/opt/m/.ATTRIBUTES/VARIABLE_VALUE (30,) | |
| [ 3.51306298e-05 3.61366037e-05 -3.67252505e-06 9.21028666e-04 | |
| 7.78463436e-04 2.24373052e-05 6.05550595e-04 7.36912712e-04 | |
| -4.31884764e-05 1.44443940e-04 1.24389135e-05 8.46692594e-04 | |
| 1.70874955e-05 3.72679904e-04 5.41794288e-05 6.08396949e-04 | |
| 1.95211032e-06 8.75406899e-04 9.23899701e-04 2.17679326e-06 | |
| 8.70055985e-04 6.87883934e-04 5.30559737e-06 5.81342028e-04 | |
| 2.78645912e-05 4.61369600e-05 7.27826264e-04 1.64074972e-05 | |
| -6.21771906e-05 1.15486218e-05] |
thierryherrmann
/ inspect_ckpt_1.py
Created
August 2, 2020 23:42
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| inspect_checkpoint(model_dir + '/variables/variables', print_values=True, | |
| variables=['model/layer_with_weights-0/bias/.OPTIMIZER_SLOT/opt/m/.ATTRIBUTES/VARIABLE_VALUE']) |
thierryherrmann
/ ckpt_out_1
Created
August 2, 2020 23:39
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| INFO:tensorflow:Assets written to: saved_model/assets | |
| non-tensor: _CHECKPOINTABLE_OBJECT_GRAPH <class 'bytes'> | |
| tensor : model/layer_with_weights-0/bias/.ATTRIBUTES/VARIABLE_VALUE (30,) | |
| tensor : model/layer_with_weights-0/bias/.OPTIMIZER_SLOT/opt/m/.ATTRIBUTES/VARIABLE_VALUE (30,) | |
| tensor : model/layer_with_weights-0/bias/.OPTIMIZER_SLOT/opt/v/.ATTRIBUTES/VARIABLE_VALUE (30,) | |
| tensor : model/layer_with_weights-0/kernel/.ATTRIBUTES/VARIABLE_VALUE (8, 30) | |
| tensor : model/layer_with_weights-0/kernel/.OPTIMIZER_SLOT/opt/m/.ATTRIBUTES/VARIABLE_VALUE (8, 30) | |
| tensor : model/layer_with_weights-0/kernel/.OPTIMIZER_SLOT/opt/v/.ATTRIBUTES/VARIABLE_VALUE (8, 30) | |
| tensor : model/variables/2/.ATTRIBUTES/VARIABLE_VALUE (30, 1) | |
| tensor : model/variables/2/.OPTIMIZER_SLOT/opt/m/.ATTRIBUTES/VARIABLE_VALUE (30, 1) |
thierryherrmann
/ save_module.py
Created
August 2, 2020 23:38
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| def save_module(module, model_dir): | |
| # When saving a tf.keras.Model with either model.save() or | |
| # tf.keras.models.save_model() or tf.saved_model.save(), | |
| # the saved model contains a `serving_default` signature used to get the | |
| # output of the model from an input sample. But here we don't save a keras | |
| # Model but a tf.Module. This requires to specify the signatures manually | |
| # Note that we also export the training function here | |
thierryherrmann
/ adam_slots_2.py
Created
August 2, 2020 23:32
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| module.opt.weights[2] | |
| <tf.Variable 'Adam/dense_2/bias/m:0' shape=(30,) dtype=float32, numpy= | |
| array([ 3.51306298e-05, 3.61366037e-05, -3.67252505e-06, 9.21028666e-04, | |
| 7.78463436e-04, 2.24373052e-05, 6.05550595e-04, 7.36912712e-04, | |
| -4.31884764e-05, 1.44443940e-04, 1.24389135e-05, 8.46692594e-04, | |
| 1.70874955e-05, 3.72679904e-04, 5.41794288e-05, 6.08396949e-04, | |
| 1.95211032e-06, 8.75406899e-04, 9.23899701e-04, 2.17679326e-06, | |
| 8.70055985e-04, 6.87883934e-04, 5.30559737e-06, 5.81342028e-04, | |
| 2.78645912e-05, 4.61369600e-05, 7.27826264e-04, 1.64074972e-05, |
thierryherrmann
/ train_2.py
Created
August 2, 2020 23:29
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| loss_hist = train_module(module, train_dataset, valid_dataset) | |
| plot_loss(loss_hist) |
thierryherrmann
/ adam_slots.py
Created
August 2, 2020 23:27
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| module.opt.weights[2] | |
| <tf.Variable 'Adam/dense_2/bias/m:0' shape=(30,) dtype=float32, numpy= | |
| array([ 1.3742445e-04, 3.0024436e-05, 7.1526818e-05, -1.0563848e-03, | |
| -2.0427089e-03, 7.6999364e-05, -3.1418181e-03, -2.4974323e-03, | |
| 3.2060378e-04, -3.7756050e-04, 1.7517927e-04, -1.3496901e-03, | |
| 3.1575797e-05, -1.4640440e-03, 1.7805261e-04, -7.5319828e-04, | |
| 2.4552579e-04, -3.8849441e-03, -1.3961941e-03, 1.4816693e-05, | |
| -4.0749349e-03, -8.9195929e-04, 1.1976792e-04, -5.5552716e-04, | |
| 2.1161152e-04, 1.3880052e-04, -1.4332745e-03, 1.2115676e-04, |
thierryherrmann
/ train_module.py
Created
August 2, 2020 23:18
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| def train_module(module, train_dataset, valid_dataset): | |
| valid_metric = keras.metrics.MeanSquaredError() | |
| loss_hist = [] | |
| step=1 | |
| for epoch in range(3): | |
| for X, y in train_dataset: | |
| loss = module.my_train(X, y) | |
| loss_hist.append(loss.numpy()) | |
| if step % 100 == 0: |