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@ -94,7 +94,7 @@ def time_series_dnn_classification(dataset, k, n0, x_columns, y_columns): |
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y = np.array(y) |
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y = np.array(y) |
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model = tf.keras.Sequential([ |
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model = tf.keras.Sequential([ |
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tf.keras.layers.Dense(32, input_dim=input_shape,activation=tf.nn.softmax), |
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tf.keras.layers.Dense(32, input_dim=input_shape,activation=tf.nn.softmax), |
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tf.keras.layers.Dense(output_shape, activation=tf.nn.softmax) |
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tf.keras.layers.Dense(output_shape, input_dim=32, activation=tf.nn.softmax) |
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]) |
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]) |
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model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy']) |
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model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy']) |
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print(output_shape) |
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print(output_shape) |
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@ -195,7 +195,7 @@ def time_series_dnn_regressions(dataset, k, n0, x_columns, y_columns): |
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tf.keras.layers.Dense(output_shape) |
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tf.keras.layers.Dense(output_shape) |
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]) |
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]) |
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model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy']) |
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model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy']) |
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model.fit(x, y, epochs=50) |
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model.fit(x, y, epochs=100, verbose=0) |
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loss, accuracy = model.evaluate(x, y) |
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loss, accuracy = model.evaluate(x, y) |
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print(loss, accuracy) |
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print(loss, accuracy) |
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pred = model.predict(x) |
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pred = model.predict(x) |
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@ -207,11 +207,11 @@ def time_series_dnn_regressions(dataset, k, n0, x_columns, y_columns): |
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def main(): |
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def main(): |
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filename = "personal.csv" |
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filename = "personal.csv" |
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df = pd.read_csv(filename) |
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df = pd.read_csv(filename) |
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x = ["day", "playerID", "fatigueSliding"] |
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y = ["day", "playerID", "BestOutOfMyselfAbsolutely", "BestOutOfMyselfSomewhat", "BestOutOfMyselfNotAtAll", "BestOutOfMyselfUnknown"] |
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x = ["day", "playerID", "DailyLoadSliding", "sleepQuality"] |
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y = ["day", "playerID", "fatigueNorm"] |
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k = 0 |
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k = 0 |
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n0 = 30 |
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n0 = 30 |
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weights = time_series_dnn_classification(df, k, n0, x, y) |
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weights = time_series_linear_regression(df, k, n0, x, y) |
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if __name__ == "__main__": |
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if __name__ == "__main__": |
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