Merge branch 'main' of https://github.com/aertslab/CREsted

merging to main

.gitignore

@@ -7,3 +7,5 @@ wandb/
 _build
 _autosummary
 node_modules
+.DS_Store
+._.DS_Store

docs/api/tools/zoo.md

@@ -14,5 +14,6 @@ Supply these (or your own) to `tl.Crested(...)` to use them in training.
     basenji
     chrombpnet
     deeptopic_cnn
+    deeptopic_lstm
     simple_convnet
-```
+```

src/crested/tl/zoo/__init__.py

@@ -1,4 +1,5 @@
 from ._basenji import basenji
 from ._chrombpnet import chrombpnet
 from ._deeptopic_cnn import deeptopic_cnn
+from ._deeptopic_lstm import deeptopic_lstm
 from ._simple_convnet import simple_convnet

src/crested/tl/zoo/_deeptopic_lstm.py

@@ -0,0 +1,110 @@
+"""Deeptopic LSTM model architecture."""
+
+import keras
+import pickle
+
+from crested.tl.zoo.utils import get_output
+
+
+def deeptopic_lstm(
+    seq_len: int,
+    num_classes: int,
+    filters: int = 300,
+    first_kernel_size: int = 30,
+    max_pool_size: int = 15,
+    max_pool_stride: int = 5,
+    dense_out: int = 256,
+    lstm_out: int = 128,
+    first_activation: str = "relu",
+    activation: str = "relu",
+    lstm_do: float = 0.1,
+    dense_do: float = 0.4,
+    pre_dense_do: float = 0.2,
+    motifs_path: str = None,
+) -> keras.Model:
+    """
+    Construct a DeepTopicLSTM model. Usually used for topic classification.
+
+    Parameters
+    ----------
+    seq_len
+        Width of the input region.
+    num_classes
+        Number of classes to predict.
+    filters
+        Number of filters in the first convolutional layer.
+        Followed by halving in subsequent layers.
+    first_kernel_size
+        Size of the kernel in the first convolutional layer.
+    max_pool_size
+        Size of the max pooling kernel.
+    max_pool_stride
+        Stride of the max pooling kernel.
+    dense_out
+        Number of neurons in the dense layer.
+    lstm_out
+        Number of units in the lstm layer.
+    first_activation
+        Activation function for the first conv block.
+    activation
+        Activation function for subsequent blocks.
+    lstm_do
+        Dropout rate for the lstm layer.
+    dense_do
+        Dropout rate for the dense layers.
+    pre_dense_do
+        Dropout rate before the dense layers.
+    motifs_path
+        Path to the motif file to initialize the convolutional weights.
+
+    Returns
+    -------
+    keras.Model
+        A Keras model.
+    """
+    inputs = keras.layers.Input(shape=(seq_len, 4), name="sequence")
+
+    hidden_layers = [
+        keras.layers.Convolution1D(
+            filters=filters,
+            kernel_size=first_kernel_size,
+            activation=first_activation,
+            padding="valid",
+            kernel_initializer='random_uniform'
+        ),
+        keras.layers.MaxPooling1D(
+            pool_size=max_pool_size,
+            strides=max_pool_stride,
+            padding='valid'
+        ),
+        keras.layers.Dropout(pre_dense_do),
+        keras.layers.TimeDistributed(keras.layers.Dense(lstm_out, activation=activation)),
+        keras.layers.Bidirectional(keras.layers.LSTM(
+            lstm_out,
+            dropout=lstm_do,
+            recurrent_dropout=lstm_do,
+            return_sequences=True)
+        ),
+        keras.layers.Dropout(pre_dense_do),
+        keras.layers.Flatten(),
+        keras.layers.Dense(dense_out, activation=activation),
+        keras.layers.Dropout(dense_do),
+        keras.layers.Dense(num_classes, activation='sigmoid')
+    ]
+
+    outputs = get_output(inputs, hidden_layers)
+
+    model = keras.Model(inputs=inputs, outputs=outputs)
+
+    if motifs_path is not None:
+        f = open(motifs_path, "rb")
+        motif_dict = pickle.load(f)
+        f.close()
+        conv_weights = model.layers[2].get_weights()
+
+        for i, name in enumerate(motif_dict):
+            conv_weights[0][:, :, i] = conv_weights[0][:, :, i] * 0.1
+            conv_weights[0][int((30 - len(motif_dict[name])) / 2):int((30 - len(motif_dict[name])) / 2) + len(motif_dict[name]), :, i] = motif_dict[name]
+        model.layers[2].set_weights(conv_weights)
+
+    return model

src/crested/tl/zoo/utils/_layers.py

@@ -9,6 +9,7 @@
     "dense_block",
     "conv_block",
     "activate",
+    "get_output",
     "conv_block_bs",
     "dilated_residual",
 ]
@@ -205,6 +206,29 @@ def activate(
     return current
 
 
+def get_output(input_layer, hidden_layers):
+    """
+    Pass input layer through hidden layers.
+
+    Parameters
+    ----------
+    input_layer
+        Input layer.
+    hidden_layers
+        Hidden layers.
+
+    Returns
+    -------
+    tf.Tensor
+        Output tensor after passing through all hidden layers.
+    """
+    output = input_layer
+    for hidden_layer in hidden_layers:
+        output = hidden_layer(output)
+
+    return output
+
+
 def conv_block_bs(
     inputs,
     filters: int | None = None,