models_no_regularization.py

from tensorflow import keras

METRICS = [
    keras.metrics.TruePositives(name='tp'),
    keras.metrics.FalsePositives(name='fp'),
    keras.metrics.TrueNegatives(name='tn'),
    keras.metrics.FalseNegatives(name='fn'), 
    # Precision: (TP) / (TP + FP)
    # what proportion of predicted Positives is truly Positive
    keras.metrics.Precision(name='precision'),
    # Recall: (TP) / (TP + FN)
    # what proportion of actual Positives is correctly classified
    keras.metrics.Recall(name='recall'),
    keras.metrics.AUC(name='auc'),
    keras.metrics.Accuracy(name='accuracy')
    ]


def model_simplest(MAX_TIMESTEPS, OH_DIMENSION):
    model_simplest = keras.models.Sequential([
    keras.layers.LSTM(32, dropout=0.2, recurrent_dropout=0.2, input_shape=[MAX_TIMESTEPS, OH_DIMENSION]),
    keras.layers.Dense(units=1, activation='sigmoid')
    ])
    model_simplest.compile(optimizer='adam', loss='binary_crossentropy', metrics=METRICS)
    return model_simplest


def model_dd(MAX_TIMESTEPS, OH_DIMENSION):
    model = keras.models.Sequential([
    keras.layers.LSTM(32, dropout=0.2, recurrent_dropout=0.2, input_shape=[MAX_TIMESTEPS, OH_DIMENSION]),
    keras.layers.Dense(32, activation='relu'),
    keras.layers.Dense(1, activation='sigmoid')
    ])
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=METRICS)
    return model


def model_deep(MAX_TIMESTEPS, OH_DIMENSION):
    model = keras.models.Sequential([
    keras.layers.LSTM(128, dropout=0.2, recurrent_dropout=0.2, input_shape=[MAX_TIMESTEPS, OH_DIMENSION]),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(16, activation='relu'),
    keras.layers.Dense(1, activation='sigmoid')
    ])
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=METRICS)
    return model


def model_deeper(MAX_TIMESTEPS, OH_DIMENSION):
    model = keras.models.Sequential([
    keras.layers.LSTM(128, dropout=0.2, recurrent_dropout=0.2, 
                      input_shape=[MAX_TIMESTEPS, OH_DIMENSION], return_sequences=True),
    keras.layers.LSTM(128, dropout=0.2, recurrent_dropout=0.2, input_shape=[MAX_TIMESTEPS, OH_DIMENSION]),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(16, activation='relu'),
    keras.layers.Dense(1, activation='sigmoid')
    ])
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=METRICS)
    return model


def model_conv_rnn(MAX_TIMESTEPS, OH_DIMENSION):
    model = keras.models.Sequential([
                keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu', 
                                    input_shape=[MAX_TIMESTEPS, OH_DIMENSION]),
                keras.layers.MaxPooling1D(pool_size=2),
                keras.layers.LSTM(64),
                keras.layers.Dense(32, activation='relu'),
                keras.layers.Dense(1, activation='sigmoid')
    ])
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=METRICS)
    return model


def model_conv_birnn(MAX_TIMESTEPS, OH_DIMENSION):
    model = keras.models.Sequential([
                keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=[MAX_TIMESTEPS, OH_DIMENSION]),
                keras.layers.MaxPooling1D(pool_size=2),
                keras.layers.Bidirectional(keras.layers.LSTM(64, dropout=0.2, recurrent_dropout=0.2)),
                keras.layers.Dense(32, activation='relu'),
                keras.layers.Dense(1, activation='sigmoid')
    ])
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=METRICS)
    return model