Original HopefullNet Architecture

[ambitious-octopus]

class HopefullNet(tf.keras.Model):
    """
    Original HopeFullNet
    """
    def __init__(self, inp_shape = (640,2)):
        super(HopefullNet, self).__init__()
        self.inp_shape = inp_shape

        self.kernel_size_0 = 20
        self.kernel_size_1 = 6
        self.drop_rate = 0.5

        self.conv1 = tf.keras.layers.Conv1D(filters=32,
                                            kernel_size=self.kernel_size_0,
                                            activation='relu',
                                            padding= "same",
                                            input_shape=self.inp_shape)
        self.batch_n_1 = tf.keras.layers.BatchNormalization()
        self.conv2 = tf.keras.layers.Conv1D(filters=32,
                                            kernel_size=self.kernel_size_0,
                                            activation='relu',
                                            padding= "valid")
        self.batch_n_2 = tf.keras.layers.BatchNormalization()
        self.spatial_drop_1 = tf.keras.layers.SpatialDropout1D(self.drop_rate)
        self.conv3 = tf.keras.layers.Conv1D(filters=32,
                                            kernel_size=self.kernel_size_1,
                                            activation='relu',
                                            padding= "valid")
        self.avg_pool1 = tf.keras.layers.AvgPool1D(pool_size=2)
        self.conv4 = tf.keras.layers.Conv1D(filters=32,
                                            kernel_size=self.kernel_size_1,
                                            activation='relu',
                                            padding= "valid")
        self.spatial_drop_2 = tf.keras.layers.SpatialDropout1D(self.drop_rate)
        self.flat = tf.keras.layers.Flatten()
        self.dense1 = tf.keras.layers.Dense(296, activation='relu')
        self.dropout1 = tf.keras.layers.Dropout(self.drop_rate)
        self.dense2 = tf.keras.layers.Dense(148, activation='relu')
        self.dropout2 = tf.keras.layers.Dropout(self.drop_rate)
        self.dense3 = tf.keras.layers.Dense(74, activation='relu')
        self.dropout3 = tf.keras.layers.Dropout(self.drop_rate)
        self.out = tf.keras.layers.Dense(5, activation='softmax')