Mercurial > repos > iuc > virhunter
diff models/model_7.py @ 0:457fd8fd681a draft
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
author | iuc |
---|---|
date | Wed, 09 Nov 2022 12:19:26 +0000 |
parents | |
children |
line wrap: on
line diff
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/models/model_7.py Wed Nov 09 12:19:26 2022 +0000 @@ -0,0 +1,49 @@ +from tensorflow.keras import layers, models + + +def launch(input_layer, hidden_layers): + output = input_layer + for hidden_layer in hidden_layers: + output = hidden_layer(output) + return output + + +def model(length, kernel_size=7, filters=256, dense_ns=256): + forward_input = layers.Input(shape=(length, 4)) + reverse_input = layers.Input(shape=(length, 4)) + hidden_layers = [ + layers.Conv1D(filters=filters, kernel_size=kernel_size), + layers.LeakyReLU(alpha=0.1), + layers.GlobalMaxPooling1D(), + layers.Dropout(0.1), + ] + forward_output = launch(forward_input, hidden_layers) + reverse_output = launch(reverse_input, hidden_layers) + output = layers.Concatenate()([forward_output, reverse_output]) + output = layers.Dense(dense_ns, activation='relu')(output) + output = layers.Dropout(0.1)(output) + # output = layers.Dense(128, activation='relu')(output) + # output = layers.Dropout(0.1)(output) + output = layers.Dense(3, activation='softmax')(output) + model_ = models.Model(inputs=[forward_input, reverse_input], outputs=output) + model_.compile(optimizer="adam", loss='categorical_crossentropy', metrics='accuracy') + return model_ + + +# def model(length, kernel_size=7, filters=256, dense_ns=512): +# forward_input = layers.Input(shape=(length, 4)) +# reverse_input = layers.Input(shape=(length, 4)) +# hidden_layers = [ +# layers.Conv1D(filters=filters, kernel_size=kernel_size), +# layers.MaxPool1D(pool_size=50, strides=25), +# layers.LSTM(32), +# ] +# forward_output = launch(forward_input, hidden_layers) +# reverse_output = launch(reverse_input, hidden_layers) +# output = layers.Concatenate()([forward_output, reverse_output]) +# # output = layers.Dense(64, activation='relu')(output) +# output = layers.Dropout(0.1)(output) +# output = layers.Dense(3, activation='softmax')(output) +# model_ = models.Model(inputs=[forward_input, reverse_input], outputs=output) +# model_.compile(optimizer="adam", loss='categorical_crossentropy', metrics='accuracy') +# return model_