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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,24 +1,81 @@ | ||
| import numpy as np | ||
| import tensorflow as tf | ||
| from keras import models, layers, losses, regularizers | ||
| import pandas as pd | ||
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| # loading the exercise reference data. | ||
| data_reference = pd.read_csv("src/resources/data/exercise_raw.csv", index_col=False) | ||
| # To keep a reference of the exercise names. | ||
| exercise_data = data_reference.iloc[:, 1].values | ||
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| class RecommenderEngine: | ||
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| def __init__(self) -> None: | ||
| self.model = None | ||
| pass | ||
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| def handle_model_creation(self): | ||
| def handle_model_creation(self, train): | ||
| ''' | ||
| Do model creation part | ||
| Create the model, or load from the file | ||
| ''' | ||
| pass | ||
| model = models.Sequential() | ||
| # Adding the Conv2D layer to the model with fileters = 8, kernel size = (3, 3), strides = (1,1), padding='same', activation='relu' and a L2 Regularization of 0.0001. | ||
| model.add(layers.Conv2D(filters = 8, kernel_size = (3,3), strides = (1,1), padding='same', activation='relu', input_shape = train.shape[1:], kernel_regularizer = regularizers.l2(0.0001))) | ||
| # Adding the Conv2D layer to the model with filters = 16, kernel_size = (3,3), strides = (1,1), padding='same', activation='relu' and a L2 Regularization of 0.0001. | ||
| model.add(layers.Conv2D(filters = 16, kernel_size = (3,3), strides = (1,1), padding='same', activation='relu', kernel_regularizer = regularizers.l2(0.0001))) | ||
| # Adding the Max Pooling layer with a pool size of (2,2), strides = (2,2). | ||
| model.add(layers.MaxPooling2D(pool_size = (2,2), strides = (2,2))) | ||
| # Adding the Conv2D layer to the model with filters = 32, kernel_size = (3,3), strides = (1,1), padding='same', activation='relu' and a L2 Regularization of 0.0001. | ||
| model.add(layers.Conv2D(filters = 32, kernel_size = (3,3), strides = (1,1), padding='same', activation='relu', kernel_regularizer = regularizers.l2(0.0001))) | ||
| # Adding the Conv2D layer to the model with filters = 64, kernel_size = (3,3), strides = (1,1), padding='same', activation='relu' and a L2 Regularization of 0.0001. | ||
| model.add(layers.Conv2D(filters = 64, kernel_size = (3,3), strides = (1,1), padding='same', activation='relu', kernel_regularizer = regularizers.l2(0.0001))) | ||
| # Adding the Max Pooling layer with a pool size of (2,2), strides = (2,2). | ||
| model.add(layers.MaxPooling2D(pool_size = (2,2), strides = (2,2))) | ||
| # Adding a flatten layer to the model. | ||
| model.add(layers.Flatten()) | ||
| # Adding a dense layer to the model with units = 512, activation='relu' and L2 Regularization of 0.0001. | ||
| model.add(layers.Dense(units = 512, activation='relu', kernel_regularizer = regularizers.l2(0.0001))) | ||
| # Adding a dense layer to the model with units = 10, activation='linear' and L2 Regularization of 0.0001. | ||
| model.add(layers.Dense(units = 50, activation='linear', kernel_regularizer = regularizers.l2(0.0001))) | ||
| # Adding a softmax layer to the output layer. | ||
| model.add(layers.Activation('softmax')) | ||
| # Compiling the Neural Network model with adam optimizer, loss = losses.categorical_crossentropy and metrics as 'accuracy'. | ||
| model.compile(optimizer = 'adam', loss = losses.categorical_crossentropy, metrics = ['accuracy']) | ||
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| return model | ||
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| def train(self): | ||
| def train(self, train, test): | ||
| ''' | ||
| A wrapper for training if needed | ||
| ''' | ||
| pass | ||
| predicted_labels = None | ||
| if train.shape[0] % 21 == 0: | ||
| model = self.handle_model_creation(train) | ||
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| history = model.fit(x = train, y = train, epochs = 10, batch_size = 1, validation_split = 0.2) | ||
| # Predict the probabilities for each class in the output layer | ||
| predictions = model.predict(test) | ||
| predicted_labels = np.argmax(predictions, axis=1) | ||
| else: | ||
| # No training happening if the data count is not above 21 rows. | ||
| pass | ||
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| return predicted_labels | ||
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| def predict(self) -> str: | ||
| def predict(self, exercise_data, train, test) -> str: | ||
| ''' | ||
| A wrapper for predicting if needed | ||
| ''' | ||
| pass | ||
| probabilities = self.train(train, test) | ||
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| # Combine the two lists using zip() | ||
| combined = list(zip(exercise_data, probabilities)) | ||
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| # Sort the combined list based on the exersise probabilites (in descending order) | ||
| sorted_combined = sorted(combined, key=lambda x: x[1], reverse=True) | ||
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| # Get the top 5 exercise recommendataions | ||
| top_5 = sorted_combined[:5] | ||
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| return top_5 |