mikkokotila
commented
4 years ago Thanks for taking the time to create a well-structured issue. I understand now what is the problem; Keras for some reason does not allow loading model from json if the name of an object (e.g. layer) is unknown to it.
It is anyhow possible to find the best models from the experiment and "recover" the models. Because this is kind of a case that can come up for several reasons, I've added a utility for doing it in a straightforward manner. You can access it by upgrading to v0.6.4 with:
pip install git+https://github.com/autonomio/talos
It will work on experiment log from any previous Talos version as well. I've created a code-complete notebook that outlines the process you can follow to get to cross-validated results for n models, as well as the models themselves. It does involve re-training n models, which depending on your architecture and system configuration may take longer than merely evaluating best models from the scan_object would.
Related with the underlying problem you suffer from, I'm not sure why Keras is doing a string check, as the meaningful part of the model is stored in the weights and not in the json. Might be a good idea to post this issue separately to Keras in case someone there warms up to the idea of making this go away.
bjtho08
ruhollah2
Overview
I built a model in Keras using the functional API. I also use the
keras_contribandkeras_radamlibraries to add new activations (Swish) and optimizers (RAdam) not yet implemented inkeras. Talos initializes and trains all iterations of the model without issue, but if I want to recall the best model or deploy the model, it fails with an error fromkeras.utils.generic_utils.deserialize_keras_object(). The error in question isValueError: Unknown layer: Swish.Prerequisites
Expected behavior
scan_object.best_model(metric="acc")should result in a new instance of the best performing model.Actual behavior
Model details
MWE
```python import keras from keras.models import Model from keras.layers import Input, Conv2D, BatchNormalization from keras.layers.advanced_activations import ReLU import talos as ta def u_net(shape, nb_filters=64, conv_size=3, init="glorot_uniform", activation=ReLU, output_channels=5): i = Input(shape, name="input_layer") n = Conv2D(nb_filters, conv_size, padding="same", kernel_initializer=init, name="block1_conv1")(i) n = activation(name="block1_{}1".format(activation.__name__))(n) n = BatchNormalization(name="block1_bn1")(n) n = Conv2D(nb_filters, conv_size, padding="same", kernel_initializer=init, name="block1_conv2")(n) n = activation(name="block1_{}2".format(activation.__name__))(n) n = BatchNormalization(name="block1_bn2")(n) o = Conv2D(output_channels, 1, activation="softmax", name="conv_out")(n) return Model(inputs=i, outputs=o) def talos_model(): model = u_net(SHAPE, nb_filters=p["nb_filters"], activation=p["act"]) model.compile(optimizer=p["opt"](lr=1e-4)) history = model.fit(x=X, y=Y) return model, history scan_object = ta.Scan(x=X, y=Y, model=talos_model, params=p) ```parameter dictionary
```python # fit params from keras.optimizers import Adam from keras.layers.advanced_activations import ReLU from keras_radam import RAdam from keras_contrib.layers.advanced_activations.swish import Swish p = { "nb_filters": [12, 16, 32], "act": [Swish, ReLU], "opt": [RAdam, Adam] } ```Scan()commandI chose to leave out sample data because it is not relevant to the issue at hand. For the same reason, I chose to create a Minimal Working Example rather than pasting the entire model, which is quite complicated and does not help to locate the issue.