Note

Go to the end to download the full example code

Tensorflow/Keras#

Note

This example requires the tensorflow package to be installed.

Theoretically, tpcp is framework agnostic and can be used with any framework. However, due to the way some frameworks handle their objects, some special handling internally is required. Hence, this example does not only serve as example on how to use tensorflow with tpcp, but also as a test case for these special cases.

When using tpcp with any machine learning framework, you either want to use a pretrained model with a normal pipeline or a train your own model as part of an Optimizable Pipeline. Here we show the second case, as it is more complex, and you are likely able to figure out the first case yourself.

This means, we are planning to perform the following steps:

  1. Create a pipeline that creates and trains a model.

  2. Allow the modification of model hyperparameters.

  3. Run a simple cross-validation to demonstrate the functionality.

This example reimplements the basic MNIST example from the [tensorflow documentation](https://www.tensorflow.org/tutorials/keras/classification).

Some Notes#

In this example we show how to implement a Pipeline that uses tensorflow. You could implement an Algorithm in a similar way. This would actually be easier, as no specific handling of the input data would be required. For a pipeline, we need to create a custom Dataset class, as this is the expected input for a pipeline.

The Dataset#

We are using the normal fashion MNIST dataset for this example It consists of 60.000 images of 28x28 pixels, each with a label. We will ignore the typical train-test split, as we want to do our own cross-validation.

In addition, we will simulate an additional “index level”. In this (and most typical deep learning datasets), each datapoint is one vector for which we can make one prediction. In tpcp, we usually deal with datasets, where you might have multiple pieces of information for each datapoint. For example, one datapoint could be a patient, for which we have an entire time series of measurements. We will simulate this here, by creating the index of our dataset as 1000 groups each containing 60 images.

Other than that, the dataset is pretty standard. Besides the create_index method, we only need to implement the input_as_array and labels_as_array methods that allow us to easily access the data once we selected a single group.

from functools import lru_cache

import numpy as np
import pandas as pd
import tensorflow as tf
from tpcp import Dataset

tf.keras.utils.set_random_seed(812)
tf.config.experimental.enable_op_determinism()


@lru_cache(maxsize=1)
def get_fashion_mnist_data():
    # Note: We throw train and test sets together, as we don't care about the official split here.
    #       We will create our own split later.
    (train_images, train_labels), (test_images, test_labels) = (
        tf.keras.datasets.fashion_mnist.load_data()
    )
    return np.array(list(train_images) + list(test_images)), list(
        train_labels
    ) + list(test_labels)


class FashionMNIST(Dataset):
    def input_as_array(self) -> np.ndarray:
        self.assert_is_single(None, "input_as_array")
        group_id = int(self.group_label.group_id)
        images, _ = get_fashion_mnist_data()
        return (
            images[group_id * 60 : (group_id + 1) * 60].reshape((60, 28, 28))
            / 255
        )

    def labels_as_array(self) -> np.ndarray:
        self.assert_is_single(None, "labels_as_array")
        group_id = int(self.group_label.group_id)
        _, labels = get_fashion_mnist_data()
        return np.array(labels[group_id * 60 : (group_id + 1) * 60])

    def create_index(self) -> pd.DataFrame:
        # There are 60.000 images in total.
        # We simulate 1000 groups of 60 images each.
        return pd.DataFrame({"group_id": list(range(1000))})

We can see our Dataset works as expected:

dataset = FashionMNIST()
dataset[0].input_as_array().shape

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz

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(60, 28, 28)

dataset[0].labels_as_array().shape

(60,)

The Pipeline#

We will create a pipeline that uses a simple neural network to classify the images. In tpcp, all “things” that should be optimized need to be parameters. This means our model itself needs to be a parameter of the pipeline. However, as we don’t have the model yet, as its creation depends on other hyperparameters, we add it as an optional parameter initialized with None. Further, we prefix the parameter name with an underscore, to signify, that this is not a parameter that should be modified manually by the user. This is just convention, and it is up to you to decide how you want to name your parameters.

We further introduce a hyperparameter n_dense_layer_nodes to show how we can influence the model creation.

The optimize method#

To make our pipeline optimizable, it needs to inherit from OptimizablePipeline. Further we need to mark at least one of the parameters as OptiPara using the type annotation. We do this for our _model parameter.

Finally, we need to implement the self_optimize method. This method will get the entire training dataset as input and should update the _model parameter with the trained model. Hence, we first extract the relevant data (remember, each datapoint is 60 images), by concatinating all images over all groups in the dataset. Then we create the Keras model based on the hyperparameters. Finally, we train the model and update the _model parameter.

Here we chose to wrap the method with make_optimize_safe. This decorator will perform some runtime checks to ensure that the method is implemented correctly.

The run method#

The run method expects that the _model parameter is already set (i.e. the pipeline was already optimized). It gets a single datapoint as input (remember, a datapoint is a single group of 60 images). We then extract the data from the datapoint and let the model make a prediction. We store the prediction on our output attribute predictions_. The trailing underscore is a convention to signify, that this is an “result” attribute.

import warnings
from typing import Optional

from tpcp import (
    OptimizablePipeline,
    OptiPara,
    make_action_safe,
    make_optimize_safe,
)
from typing_extensions import Self


class KerasPipeline(OptimizablePipeline):
    n_dense_layer_nodes: int
    n_train_epochs: int
    _model: OptiPara[Optional[tf.keras.Sequential]]

    predictions_: np.ndarray

    def __init__(
        self,
        n_dense_layer_nodes=128,
        n_train_epochs=5,
        _model: Optional[tf.keras.Sequential] = None,
    ):
        self.n_dense_layer_nodes = n_dense_layer_nodes
        self.n_train_epochs = n_train_epochs
        self._model = _model

    @property
    def predicted_labels_(self):
        return np.argmax(self.predictions_, axis=1)

    @make_optimize_safe
    def self_optimize(self, dataset, **_) -> Self:
        data = tf.convert_to_tensor(
            np.vstack([d.input_as_array() for d in dataset])
        )
        labels = tf.convert_to_tensor(
            np.hstack([d.labels_as_array() for d in dataset])
        )

        print(data.shape)
        if self._model is not None:
            warnings.warn("Overwriting existing model!")

        self._model = tf.keras.Sequential(
            [
                tf.keras.layers.Input((28, 28)),
                tf.keras.layers.Flatten(),
                tf.keras.layers.Dense(
                    self.n_dense_layer_nodes, activation="relu"
                ),
                tf.keras.layers.Dense(10),
            ]
        )

        self._model.compile(
            optimizer="adam",
            loss=tf.keras.losses.SparseCategoricalCrossentropy(
                from_logits=True
            ),
            metrics=["accuracy"],
        )

        self._model.fit(data, labels, epochs=self.n_train_epochs)

        return self

    @make_action_safe
    def run(self, datapoint) -> Self:
        if self._model is None:
            raise RuntimeError("Model not trained yet!")
        data = tf.convert_to_tensor(datapoint.input_as_array())

        self.predictions_ = self._model.predict(data)
        return self

Testing the pipeline#

We can now test our pipeline. We will run the optimization using a couple of datapoints (to keep everything fast) and then use run to get the predictions for a single unseen datapoint.

pipeline = KerasPipeline().self_optimize(FashionMNIST()[:10])
p1 = pipeline.run(FashionMNIST()[11])
print(p1.predicted_labels_)
print(FashionMNIST()[11].labels_as_array())

(600, 28, 28)
Epoch 1/5

 1/19 ━━━━━━━━━━━━━━━━━━━━ 9s 536ms/step - accuracy: 0.1250 - loss: 2.2925
 2/19 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.1406 - loss: 2.2431  
19/19 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3623 - loss: 1.8187
Epoch 2/5

 1/19 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.7188 - loss: 1.0003
19/19 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.6958 - loss: 0.9131
Epoch 3/5

 1/19 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.7812 - loss: 0.7427
19/19 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.7586 - loss: 0.7140
Epoch 4/5

 1/19 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.7812 - loss: 0.6089
19/19 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.7986 - loss: 0.5833
Epoch 5/5

 1/19 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.7812 - loss: 0.5329
19/19 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8290 - loss: 0.5055

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step
[8 8 6 9 4 0 7 3 7 9 4 8 4 3 7 8 1 4 0 7 9 8 5 5 2 1 3 4 1 9 7 5 9 9 7 8 2
 7 4 7 2 4 7 1 1 7 5 4 8 3 5 9 0 7 4 0 0 9 1 9]
[8 8 0 9 2 0 7 3 7 9 3 8 4 3 7 8 1 4 0 7 9 8 5 5 2 1 3 4 6 7 7 5 9 9 7 8 2
 7 4 7 0 3 5 1 1 5 5 2 8 3 5 9 0 7 3 0 0 7 1 9]

We can see that even with just 5 epochs, the model already performs quite well. To quantify we can calculate the accuracy for this datapoint:

from sklearn.metrics import accuracy_score

accuracy_score(p1.predicted_labels_, FashionMNIST()[11].labels_as_array())

0.8

Cross Validation#

If we want to run a cross validation, we need to formalize the scoring into a function. We will calculate two types of accuracy: First, the accuracy per group and second, the accuracy over all images across all groups. For more information about how this works, check the Custom Scorer example.

from collections.abc import Sequence

from tpcp.validate import Aggregator


class SingleValueAccuracy(Aggregator[tuple[np.ndarray, np.ndarray]]):
    def aggregate(
        self, /, values: Sequence[tuple[np.ndarray, np.ndarray]], **_
    ) -> dict[str, float]:
        return {
            "accuracy": accuracy_score(
                np.hstack([v[0] for v in values]),
                np.hstack([v[1] for v in values]),
            )
        }


single_value_accuracy = SingleValueAccuracy()


def scoring(pipeline, datapoint):
    result: np.ndarray = pipeline.safe_run(datapoint).predicted_labels_
    reference = datapoint.labels_as_array()

    return {
        "accuracy": accuracy_score(result, reference),
        "per_sample": single_value_accuracy((result, reference)),
    }

Now we can run a cross validation. We will only run it on a subset of the data, to keep the runtime manageable.

Note

You might see warnings about retracing of the model. This is because we clone the pipeline before each call to the run method. This is a good idea to ensure that all pipelines are independent of each other, however, might result in some performance overhead.

from tpcp.optimize import Optimize
from tpcp.validate import cross_validate

pipeline = KerasPipeline(n_train_epochs=10)
cv_results = cross_validate(
    Optimize(pipeline), FashionMNIST()[:100], scoring=scoring, cv=3
)

CV Folds:   0%|          | 0/3 [00:00<?, ?it/s](3960, 28, 28)
Epoch 1/10

  1/124 ━━━━━━━━━━━━━━━━━━━━ 1:01 498ms/step - accuracy: 0.0938 - loss: 2.5074
 49/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.4249 - loss: 1.5674    
 99/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.5298 - loss: 1.2991
124/124 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.5611 - loss: 1.2206
Epoch 2/10

  1/124 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.8750 - loss: 0.4848
 50/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.7897 - loss: 0.6086 
101/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.7966 - loss: 0.5900
124/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.7990 - loss: 0.5862
Epoch 3/10

  1/124 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step - accuracy: 0.8438 - loss: 0.4153
 51/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8220 - loss: 0.5114 
103/124 ━━━━━━━━━━━━━━━━━━━━ 0s 993us/step - accuracy: 0.8282 - loss: 0.5012
124/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1000us/step - accuracy: 0.8293 - loss: 0.5000
Epoch 4/10

  1/124 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.8750 - loss: 0.3588
 51/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8373 - loss: 0.4516 
102/124 ━━━━━━━━━━━━━━━━━━━━ 0s 996us/step - accuracy: 0.8409 - loss: 0.4487
124/124 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - accuracy: 0.8421 - loss: 0.4488
Epoch 5/10

  1/124 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.8750 - loss: 0.3387
 52/124 ━━━━━━━━━━━━━━━━━━━━ 0s 990us/step - accuracy: 0.8512 - loss: 0.4120
103/124 ━━━━━━━━━━━━━━━━━━━━ 0s 994us/step - accuracy: 0.8546 - loss: 0.4115
124/124 ━━━━━━━━━━━━━━━━━━━━ 0s 997us/step - accuracy: 0.8557 - loss: 0.4119
Epoch 6/10

  1/124 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.8750 - loss: 0.3044
 48/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8627 - loss: 0.3748 
 99/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8631 - loss: 0.3793
124/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8638 - loss: 0.3807
Epoch 7/10

  1/124 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9062 - loss: 0.2879
 52/124 ━━━━━━━━━━━━━━━━━━━━ 0s 996us/step - accuracy: 0.8656 - loss: 0.3478
104/124 ━━━━━━━━━━━━━━━━━━━━ 0s 991us/step - accuracy: 0.8683 - loss: 0.3533
103/124 ━━━━━━━━━━━━━━━━━━━━ 0s 991us/step - accuracy: 0.8682 - loss: 0.3532
124/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8692 - loss: 0.3543
Epoch 8/10

  1/124 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.8750 - loss: 0.2663
 51/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8732 - loss: 0.3235 
100/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8769 - loss: 0.3302
124/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8780 - loss: 0.3317
Epoch 9/10

  1/124 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.9375 - loss: 0.2468
 51/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8885 - loss: 0.3007 
103/124 ━━━━━━━━━━━━━━━━━━━━ 0s 994us/step - accuracy: 0.8879 - loss: 0.3081
124/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8881 - loss: 0.3095
Epoch 10/10

  1/124 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - accuracy: 0.9375 - loss: 0.2620
 45/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8976 - loss: 0.2822 
 96/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8954 - loss: 0.2897
124/124 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8953 - loss: 0.2917


Datapoints:   0%|          | 0/34 [00:00<?, ?it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step
WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x748893d66c20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/stepWARNING:tensorflow:6 out of the last 6 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x748893d66c20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step


Datapoints:   6%|▌         | 2/34 [00:00<00:03, 10.64it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  12%|█▏        | 4/34 [00:00<00:02, 10.63it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step


Datapoints:  18%|█▊        | 6/34 [00:00<00:02, 10.71it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  24%|██▎       | 8/34 [00:00<00:02, 10.82it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step


Datapoints:  29%|██▉       | 10/34 [00:00<00:02, 10.63it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step


Datapoints:  35%|███▌      | 12/34 [00:01<00:02, 10.60it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  41%|████      | 14/34 [00:01<00:01, 10.71it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  47%|████▋     | 16/34 [00:01<00:01, 10.81it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step


Datapoints:  53%|█████▎    | 18/34 [00:01<00:01, 10.72it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step


Datapoints:  59%|█████▉    | 20/34 [00:01<00:01, 10.79it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  65%|██████▍   | 22/34 [00:02<00:01, 10.70it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step


Datapoints:  71%|███████   | 24/34 [00:02<00:00, 10.72it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step


Datapoints:  76%|███████▋  | 26/34 [00:02<00:00, 10.59it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step


Datapoints:  82%|████████▏ | 28/34 [00:02<00:00, 10.48it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step


Datapoints:  88%|████████▊ | 30/34 [00:02<00:00, 10.53it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step


Datapoints:  94%|█████████▍| 32/34 [00:03<00:00, 10.65it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step


Datapoints: 100%|██████████| 34/34 [00:03<00:00, 10.63it/s]
Datapoints: 100%|██████████| 34/34 [00:03<00:00, 10.66it/s]

CV Folds:  33%|███▎      | 1/3 [00:05<00:10,  5.50s/it](4020, 28, 28)
Epoch 1/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1:00 481ms/step - accuracy: 0.1250 - loss: 2.3656
 47/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.4549 - loss: 1.5921    
 96/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.5481 - loss: 1.3290
126/126 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.5820 - loss: 1.2309
Epoch 2/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.8438 - loss: 0.4830
 51/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.7666 - loss: 0.6231 
101/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.7752 - loss: 0.6119
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.7789 - loss: 0.6047
Epoch 3/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.8750 - loss: 0.3561
 53/126 ━━━━━━━━━━━━━━━━━━━━ 0s 975us/step - accuracy: 0.8123 - loss: 0.5234
106/126 ━━━━━━━━━━━━━━━━━━━━ 0s 981us/step - accuracy: 0.8162 - loss: 0.5179
105/126 ━━━━━━━━━━━━━━━━━━━━ 0s 970us/step - accuracy: 0.8161 - loss: 0.5179
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 991us/step - accuracy: 0.8177 - loss: 0.5147
Epoch 4/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.9375 - loss: 0.3083
 51/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8339 - loss: 0.4834 
102/126 ━━━━━━━━━━━━━━━━━━━━ 0s 993us/step - accuracy: 0.8352 - loss: 0.4792
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - accuracy: 0.8366 - loss: 0.4753
Epoch 5/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.8750 - loss: 0.3016
 51/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8383 - loss: 0.4376 
104/126 ━━━━━━━━━━━━━━━━━━━━ 0s 984us/step - accuracy: 0.8438 - loss: 0.4329
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - accuracy: 0.8458 - loss: 0.4303
Epoch 6/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.9062 - loss: 0.3036
 51/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8440 - loss: 0.4085 
102/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8522 - loss: 0.4026
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8548 - loss: 0.3997
Epoch 7/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.8750 - loss: 0.2968
 52/126 ━━━━━━━━━━━━━━━━━━━━ 0s 998us/step - accuracy: 0.8487 - loss: 0.3796
102/126 ━━━━━━━━━━━━━━━━━━━━ 0s 999us/step - accuracy: 0.8579 - loss: 0.3737
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8608 - loss: 0.3710
Epoch 8/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.8750 - loss: 0.2878
 50/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8604 - loss: 0.3550 
100/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8684 - loss: 0.3501
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8708 - loss: 0.3480
Epoch 9/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.8750 - loss: 0.2957
 46/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8609 - loss: 0.3401 
 95/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8697 - loss: 0.3334
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8734 - loss: 0.3305
Epoch 10/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step - accuracy: 0.8750 - loss: 0.2844
 48/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8724 - loss: 0.3171 
 99/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8819 - loss: 0.3105
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8847 - loss: 0.3087


Datapoints:   0%|          | 0/33 [00:00<?, ?it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step


Datapoints:   6%|▌         | 2/33 [00:00<00:02, 10.55it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step


Datapoints:  12%|█▏        | 4/33 [00:00<00:02, 10.54it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step


Datapoints:  18%|█▊        | 6/33 [00:00<00:02, 10.57it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  24%|██▍       | 8/33 [00:00<00:02, 10.84it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  30%|███       | 10/33 [00:00<00:02, 10.91it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  36%|███▋      | 12/33 [00:01<00:01, 10.88it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step


Datapoints:  42%|████▏     | 14/33 [00:01<00:01, 10.96it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  48%|████▊     | 16/33 [00:01<00:01, 11.02it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  55%|█████▍    | 18/33 [00:01<00:01, 10.85it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  61%|██████    | 20/33 [00:01<00:01, 10.88it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  67%|██████▋   | 22/33 [00:02<00:01, 10.78it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  73%|███████▎  | 24/33 [00:02<00:00, 10.72it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  79%|███████▉  | 26/33 [00:02<00:00, 10.74it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  85%|████████▍ | 28/33 [00:02<00:00,  6.74it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  91%|█████████ | 30/33 [00:03<00:00,  7.57it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  97%|█████████▋| 32/33 [00:03<00:00,  8.40it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step

Datapoints: 100%|██████████| 33/33 [00:03<00:00,  9.65it/s]

CV Folds:  67%|██████▋   | 2/3 [00:11<00:05,  5.62s/it](4020, 28, 28)
Epoch 1/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1:02 497ms/step - accuracy: 0.0938 - loss: 2.3129
 48/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.4633 - loss: 1.5562    
 97/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.5515 - loss: 1.3119
126/126 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.5833 - loss: 1.2218
Epoch 2/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.8125 - loss: 0.4789
 52/126 ━━━━━━━━━━━━━━━━━━━━ 0s 992us/step - accuracy: 0.7834 - loss: 0.6141
105/126 ━━━━━━━━━━━━━━━━━━━━ 0s 974us/step - accuracy: 0.7881 - loss: 0.6070
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 979us/step - accuracy: 0.7896 - loss: 0.6034
Epoch 3/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.8750 - loss: 0.3564
 52/126 ━━━━━━━━━━━━━━━━━━━━ 0s 984us/step - accuracy: 0.8227 - loss: 0.5140
104/126 ━━━━━━━━━━━━━━━━━━━━ 0s 981us/step - accuracy: 0.8224 - loss: 0.5146
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - accuracy: 0.8226 - loss: 0.5135
Epoch 4/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9062 - loss: 0.3024
 52/126 ━━━━━━━━━━━━━━━━━━━━ 0s 993us/step - accuracy: 0.8465 - loss: 0.4546
102/126 ━━━━━━━━━━━━━━━━━━━━ 0s 997us/step - accuracy: 0.8444 - loss: 0.4580
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 994us/step - accuracy: 0.8441 - loss: 0.4580
Epoch 5/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9062 - loss: 0.2785
 51/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8613 - loss: 0.4169 
101/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8588 - loss: 0.4207
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8581 - loss: 0.4210
Epoch 6/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - accuracy: 0.9062 - loss: 0.2679
 50/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8745 - loss: 0.3801 
 97/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8722 - loss: 0.3856
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8713 - loss: 0.3865
Epoch 7/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.9375 - loss: 0.2306
 46/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8839 - loss: 0.3508 
 90/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8821 - loss: 0.3568
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8813 - loss: 0.3593
Epoch 8/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.9375 - loss: 0.2117
 53/126 ━━━━━━━━━━━━━━━━━━━━ 0s 968us/step - accuracy: 0.8897 - loss: 0.3291
106/126 ━━━━━━━━━━━━━━━━━━━━ 0s 962us/step - accuracy: 0.8879 - loss: 0.3359
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 975us/step - accuracy: 0.8875 - loss: 0.3369
Epoch 9/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.9375 - loss: 0.2051
 51/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8944 - loss: 0.3089 
103/126 ━━━━━━━━━━━━━━━━━━━━ 0s 993us/step - accuracy: 0.8937 - loss: 0.3146
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 990us/step - accuracy: 0.8941 - loss: 0.3157
Epoch 10/10

  1/126 ━━━━━━━━━━━━━━━━━━━━ 2s 17ms/step - accuracy: 0.9375 - loss: 0.1920
 51/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8977 - loss: 0.2915 
102/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8983 - loss: 0.2979
126/126 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.8987 - loss: 0.2994


Datapoints:   0%|          | 0/33 [00:00<?, ?it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step


Datapoints:   6%|▌         | 2/33 [00:00<00:02, 11.13it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step


Datapoints:  12%|█▏        | 4/33 [00:00<00:02, 11.53it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step


Datapoints:  18%|█▊        | 6/33 [00:00<00:02, 11.34it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  24%|██▍       | 8/33 [00:00<00:02, 11.30it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step


Datapoints:  30%|███       | 10/33 [00:00<00:02, 11.33it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step


Datapoints:  36%|███▋      | 12/33 [00:01<00:01, 11.50it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step


Datapoints:  42%|████▏     | 14/33 [00:01<00:01, 11.34it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step


Datapoints:  48%|████▊     | 16/33 [00:01<00:01, 11.12it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step


Datapoints:  55%|█████▍    | 18/33 [00:01<00:01, 11.06it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step


Datapoints:  61%|██████    | 20/33 [00:01<00:01, 11.10it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step


Datapoints:  67%|██████▋   | 22/33 [00:01<00:01, 10.89it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step


Datapoints:  73%|███████▎  | 24/33 [00:02<00:00, 10.80it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step


Datapoints:  79%|███████▉  | 26/33 [00:02<00:00, 11.01it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  85%|████████▍ | 28/33 [00:02<00:00, 11.01it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


Datapoints:  91%|█████████ | 30/33 [00:02<00:00, 10.98it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step


Datapoints:  97%|█████████▋| 32/33 [00:02<00:00, 11.02it/s]
1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step

Datapoints: 100%|██████████| 33/33 [00:02<00:00, 11.09it/s]

CV Folds: 100%|██████████| 3/3 [00:16<00:00,  5.48s/it]
CV Folds: 100%|██████████| 3/3 [00:16<00:00,  5.51s/it]

We can now look at the results per group:

cv_results["test__single__accuracy"]

[[0.8333333333333334, 0.8333333333333334, 0.9166666666666666, 0.7666666666666667, 0.8, 0.8166666666666667, 0.8, 0.8333333333333334, 0.8333333333333334, 0.85, 0.7666666666666667, 0.8333333333333334, 0.8666666666666667, 0.85, 0.8833333333333333, 0.8666666666666667, 0.8333333333333334, 0.8333333333333334, 0.8, 0.8333333333333334, 0.7833333333333333, 0.7166666666666667, 0.8166666666666667, 0.8166666666666667, 0.8333333333333334, 0.8833333333333333, 0.8833333333333333, 0.85, 0.85, 0.75, 0.75, 0.85, 0.8, 0.8833333333333333], [0.8, 0.9, 0.7333333333333333, 0.8666666666666667, 0.8833333333333333, 0.8333333333333334, 0.8833333333333333, 0.8833333333333333, 0.85, 0.8333333333333334, 0.8333333333333334, 0.7833333333333333, 0.8, 0.8, 0.8, 0.7833333333333333, 0.8166666666666667, 0.75, 0.85, 0.7666666666666667, 0.8, 0.8333333333333334, 0.7666666666666667, 0.9, 0.8, 0.7833333333333333, 0.8166666666666667, 0.8333333333333334, 0.8833333333333333, 0.9166666666666666, 0.7833333333333333, 0.7833333333333333, 0.8833333333333333], [0.7833333333333333, 0.9333333333333333, 0.75, 0.8333333333333334, 0.8833333333333333, 0.8666666666666667, 0.8833333333333333, 0.9, 0.9, 0.9, 0.9166666666666666, 0.85, 0.8666666666666667, 0.8833333333333333, 0.85, 0.8, 0.9166666666666666, 0.75, 0.7166666666666667, 0.8666666666666667, 0.8166666666666667, 0.75, 0.7833333333333333, 0.7333333333333333, 0.8666666666666667, 0.8833333333333333, 0.9166666666666666, 0.8, 0.8666666666666667, 0.9, 0.85, 0.85, 0.9]]

Average first per group and then over all groups:

cv_results["test__agg__accuracy"]

array([0.82696078, 0.82525253, 0.84747475])

And the overall accuracy as the average over all samples of all groups within a fold:

cv_results["test__agg__per_sample__accuracy"]

array([0.82696078, 0.82525253, 0.84747475])

Total running time of the script: (0 minutes 28.320 seconds)

Estimated memory usage: 760 MB

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