class BeplerContactPredictor(Model):
def __init__(self,
input_name: str = 'encoder_output',
output_name: str = 'contact_prob'):
super().__init__()
self._input_name = input_name
self._output_name = output_name
def concat_pairs(tensor):
input_mul = tensor[:, :, None] * tensor[:, None, :]
input_sub = tf.abs(tensor[:, :, None] - tensor[:, None, :])
output = tf.concat((input_mul, input_sub), -1)
return output
self.get_pairwise_feature_vector = Lambda(concat_pairs)
self.predict_contact_map = Stack()
self.predict_contact_map.add(
Conv2D(32, 1, use_bias=True, padding='same', activation='relu'))
self.predict_contact_map.add(
Conv2D(1, 7, use_bias=True, padding='same', activation='linear'))
def call(self, inputs):
encoder_output = inputs[self._input_name]
tf.add_to_collection('checkpoints', encoder_output)
z = self.get_pairwise_feature_vector(encoder_output)
sequence_mask = rk.utils.convert_sequence_length_to_sequence_mask(
encoder_output, inputs['protein_length'])
mask_2d = sequence_mask[:, None, :] & sequence_mask[:, :, None]
prediction = self.predict_contact_map(z, mask=mask_2d)
inputs[self._output_name] = prediction
return inputs
def __init__(self,
n_classes: int,
input_name: str = 'encoder_output',
output_name: str = 'sequence_logits',
use_conv: bool = True) -> None:
super().__init__()
self._input_name = input_name
self._output_name = output_name
if use_conv:
self.predict_class = Stack([
LayerNorm(),
Conv1D(128,
5,
activation='relu',
padding='same',
use_bias=True),
Conv1D(n_classes,
3,
activation=None,
padding='same',
use_bias=True)
])
else:
self.predict_class = Stack([
LayerNorm(),
Dense(512, activation='relu'),
Dense(n_classes, activation=None)
])
def __init__(self,
input_name: str = 'encoder_output',
output_name: str = 'sequence_logits'):
super().__init__()
self._input_name = input_name
self._output_name = output_name
self.get_pairwise_feature_vector = Stack()
self.get_pairwise_feature_vector.add(Dense(64, activation='linear'))
def concat_pairs(tensor):
seqlen = tf.shape(tensor)[1]
input_left = tf.tile(tensor[:, :, None], (1, 1, seqlen, 1))
input_right = tf.tile(tensor[:, None, :], (1, seqlen, 1, 1))
output = tf.concat((input_left, input_right), -1)
return output
self.get_pairwise_feature_vector.add(Lambda(concat_pairs))
self.predict_contact_map = Stack()
self.predict_contact_map.add(PaddedConv(2, 64, 1, dropout=0.1))
for layer in range(30):
self.predict_contact_map.add(
ResidualBlock(2,
64,
3,
dropout=0.1,
add_checkpoint=layer % 5 == 0))
self.predict_contact_map.add(Dense(1, activation='linear'))
def __init__(self,
input_name: str = 'encoder_output',
output_name: str = 'cls_vector'):
super().__init__()
self._input_name = input_name
self._output_name = output_name
self.compute_attention = Stack([LayerNorm(), Dense(1, activation='linear'), Dropout(0.1)])
self.attention_mask = ApplyAttentionMask()
class ResidueResidueContactPredictor(Model):
def __init__(self,
input_name: str = 'encoder_output',
output_name: str = 'sequence_logits'):
super().__init__()
self._input_name = input_name
self._output_name = output_name
self.get_pairwise_feature_vector = Stack()
self.get_pairwise_feature_vector.add(Dense(64, activation='linear'))
def concat_pairs(tensor):
seqlen = tf.shape(tensor)[1]
input_left = tf.tile(tensor[:, :, None], (1, 1, seqlen, 1))
input_right = tf.tile(tensor[:, None, :], (1, seqlen, 1, 1))
output = tf.concat((input_left, input_right), -1)
return output
self.get_pairwise_feature_vector.add(Lambda(concat_pairs))
self.predict_contact_map = Stack()
self.predict_contact_map.add(PaddedConv(2, 64, 1, dropout=0.1))
for layer in range(30):
self.predict_contact_map.add(
ResidualBlock(2,
64,
3,
dropout=0.1,
add_checkpoint=layer % 5 == 0))
self.predict_contact_map.add(Dense(1, activation='linear'))
def call(self, inputs):
encoder_output = inputs[self._input_name]
tf.add_to_collection('checkpoints', encoder_output)
z = self.get_pairwise_feature_vector(encoder_output)
self.pairwise_z = z
sequence_mask = rk.utils.convert_sequence_length_to_sequence_mask(
encoder_output, inputs['protein_length'])
mask_2d = sequence_mask[:, None, :] & sequence_mask[:, :, None]
prediction = self.predict_contact_map(z, mask=mask_2d)
prediction = (prediction +
tf.transpose(prediction, (0, 2, 1, 3))) / 2 # symmetrize
inputs[self._output_name] = prediction
return inputs
def __init__(self,
d_output: int,
input_name: str = 'cls_vector',
output_name: str = 'prediction') -> None:
super().__init__()
self._d_output = d_output
self._input_name = input_name
self._output_name = output_name
self.predict_vector = Stack([LayerNorm(), Dense(512, 'relu'), Dropout(0.5), Dense(d_output)])
def build_task_model(embedding_model: Model, tasks: List[Task],
freeze_embedding_weights: bool) -> Model:
layers = [embedding_model]
if freeze_embedding_weights:
layers.append(FreezeWeights())
for task in tasks:
layers = task.build_output_model(layers)
return Stack(layers)
def __init__(self,
input_name: str = 'encoder_output',
output_name: str = 'contact_prob'):
super().__init__()
self._input_name = input_name
self._output_name = output_name
def concat_pairs(tensor):
input_mul = tensor[:, :, None] * tensor[:, None, :]
input_sub = tf.abs(tensor[:, :, None] - tensor[:, None, :])
output = tf.concat((input_mul, input_sub), -1)
return output
self.get_pairwise_feature_vector = Lambda(concat_pairs)
self.predict_contact_map = Stack()
self.predict_contact_map.add(
Conv2D(32, 1, use_bias=True, padding='same', activation='relu'))
self.predict_contact_map.add(
Conv2D(1, 7, use_bias=True, padding='same', activation='linear'))
def __init__(self, input_name: str = 'encoder_output'):
super().__init__()
self._input_name = input_name
self.convs = Stack([
PaddedConv(1, 32, 129),
PaddedConv(1, 32, 257)])
self.bilstm = Stack([
BidirectionalCudnnLSTM(1024, return_sequences=True),
BidirectionalCudnnLSTM(1024, return_sequences=True)])
# Need to predict phi, psi, rsa, disorder, ss3, ss8
num_outputs = 0
num_outputs += 1 # phi
num_outputs += 2 # psi
num_outputs += 1 # rsa
num_outputs += 1 # disorder
num_outputs += 1 # interface
num_outputs += 3 # ss3
num_outputs += 8 # ss8
self.predict_outputs = Dense(num_outputs, activation=None)
def __init__(self,
n_symbols: int,
n_units: int = 1024,
n_layers: int = 3,
dropout: Optional[float] = 0.1) -> None:
super().__init__(n_symbols)
if dropout is None:
dropout = 0
self.embedding = Embedding(n_symbols, 128)
self.forward_lstm = Stack([
LSTM(n_units,
return_sequences=True) for _ in range(n_layers)],
name='forward_lstm')
self.reverse_lstm = Stack([
LSTM(n_units,
return_sequences=True) for _ in range(n_layers)],
name='reverse_lstm')
self.dropout = Dropout(dropout)
def __init__(self, n_symbols: int, dropout: float = 0, use_pfam_alphabet: bool = True):
super().__init__()
self._use_pfam_alphabet = use_pfam_alphabet
if use_pfam_alphabet:
self.embed = Embedding(n_symbols, n_symbols)
else:
n_symbols = 21
self.embed = Embedding(n_symbols + 1, n_symbols)
self.dropout = Dropout(dropout)
self.rnn = Stack([
LSTM(1024, return_sequences=True, use_bias=True,
implementation=2, recurrent_activation='sigmoid'),
LSTM(1024, return_sequences=True, use_bias=True,
implementation=2, recurrent_activation='sigmoid')])
self.compute_logits = Dense(n_symbols, use_bias=True, activation='linear')
def __init__(self,
n_symbols: int,
dropout: float = 0,
use_pfam_alphabet: bool = True):
if not use_pfam_alphabet:
n_symbols = 21
super().__init__(n_symbols)
self._use_pfam_alphabet = use_pfam_alphabet
self.embed = LMEmbed(n_symbols, dropout)
self.dropout = Dropout(dropout)
lstm = Stack([
Bidirectional(
LSTM(512, return_sequences=True, use_bias=True,
recurrent_activation='sigmoid', implementation=2))
for _ in range(3)])
self.rnn = lstm
self.proj = Dense(100, use_bias=True, activation='linear')
self.random_replace = RandomReplaceMask(0.05, n_symbols)
def __init__(self, n_symbols, length=3000):
super().__init__(n_symbols)
self._length = length
encoder = Stack()
encoder.add(Embedding(n_symbols, 128, input_length=self._length))
encoder.add(Conv1D(256, 5, strides=1, padding='same', dilation_rate=1, activation='relu'))
encoder.add(BatchNormalization())
encoder.add(MaxPooling1D(2,2))
encoder.add(Conv1D(256, 5, strides=1, padding='same', dilation_rate=1, activation='relu'))
encoder.add(BatchNormalization())
encoder.add(MaxPooling1D(2,2))
encoder.add(Conv1D(256, 5, strides=1, padding='same', dilation_rate=1, activation='relu'))
encoder.add(BatchNormalization())
encoder.add(MaxPooling1D(2,2))
encoder.add(Conv1D(256, 5, strides=1, padding='same', dilation_rate=1, activation='relu'))
encoder.add(BatchNormalization())
encoder.add(MaxPooling1D(2,2))
encoder.add(Conv1D(256, 5, strides=1, padding='same', dilation_rate=1, activation='relu'))
encoder.add(BatchNormalization())
encoder.add(MaxPooling1D(2,2))
encoder.add(Conv1D(256, 5, strides=1, padding='same', dilation_rate=1, activation='relu'))
encoder.add(BatchNormalization())
encoder.add(MaxPooling1D(2,2))
encoder.add(Flatten())
encoder.add(Dense(1000))
decoder = Stack()
decoder.add(Dense(47*256, input_shape=(1000,), activation='relu'))
decoder.add(Reshape((47, 256)))
decoder.add(UpSampling1D(2))
decoder.add(Conv1D(256, 5, strides=1, padding='same', dilation_rate=1, activation='relu'))
decoder.add(BatchNormalization())
decoder.add(UpSampling1D(2))
decoder.add(Conv1D(256, 5, strides=1, padding='same', dilation_rate=1, activation='relu'))
decoder.add(BatchNormalization())
decoder.add(UpSampling1D(2))
decoder.add(Conv1D(256, 5, strides=1, padding='same', dilation_rate=1, activation='relu'))
decoder.add(BatchNormalization())
decoder.add(UpSampling1D(2))
decoder.add(Conv1D(256, 5, strides=1, padding='same', dilation_rate=1, activation='relu'))
decoder.add(BatchNormalization())
decoder.add(UpSampling1D(2))
decoder.add(Conv1D(256, 5, strides=1, padding='same', dilation_rate=1, activation='relu'))
decoder.add(BatchNormalization())
decoder.add(UpSampling1D(2))
decoder.add(Conv1D(256, 5, strides=1, padding='same', dilation_rate=1, activation='relu'))
decoder.add(Cropping1D((0,8)))
self.encoder = encoder
self.decoder = decoder
def __init__(self,
n_symbols: int,
n_layers: int = 35,
filters: int = 256,
kernel_size: int = 9,
layer_norm: bool = True,
activation: str = 'elu',
dilation_rate: int = 2,
dropout: Optional[float] = 0.1) -> None:
super().__init__(n_symbols)
self.n_symbols = n_symbols
self.n_layers = n_layers
self.filters = filters
self.kernel_size = kernel_size
self.layer_norm = layer_norm
self.activation = activation
self.dilation_rate = dilation_rate
self.dropout = dropout
print(self)
input_embedding = Stack()
input_embedding.add(Embedding(n_symbols, 128))
input_embedding.add(Lambda(lambda x: x * np.sqrt(filters)))
input_embedding.add(PositionEmbedding())
encoder = Stack()
encoder.add(input_embedding)
encoder.add(PaddedConv(1, filters, kernel_size, 1, activation, dropout))
encoder.add(ResidualBlock(1, filters, kernel_size, activation=activation,
dilation_rate=1, dropout=dropout))
for layer in range(n_layers - 1):
encoder.add(ResidualBlock(1, filters, kernel_size, activation=activation,
dilation_rate=dilation_rate, dropout=dropout,
add_checkpoint=layer % 5 == 0))
self.encoder = encoder
self.z_mu = PaddedConv(1, 4, kernel_size, 1, 'linear', 0.0)
self.z_var = PaddedConv(1, 4, kernel_size, 1, 'linear', 0.0)
decoder = Stack()
decoder.add(PaddedConv(1, filters, kernel_size, 1, activation, dropout))
decoder.add(ResidualBlock(1, filters, kernel_size, activation=activation,
dilation_rate=1, dropout=dropout))
for layer in range(n_layers - 1):
decoder.add(ResidualBlock(1, filters, kernel_size, activation=activation,
dilation_rate=dilation_rate, dropout=dropout,
add_checkpoint=layer % 5 == 0))
self.decoder = decoder
class MyModel(AbstractTapeModel):
@hparams.capture
def __init__(self, n_symbols, latent_size=32, max_seq_len=10000):
self.latent_size = latent_size
self.max_seq_len = max_seq_len
super().__init__(n_symbols)
self.input_embedding = Embedding(n_symbols, 128)
enc = Stack()
enc.add(
Conv1D(filters=32,
kernel_size=7,
strides=1,
dilation_rate=2,
activation='relu'))
enc.add(
Conv1D(filters=64,
kernel_size=5,
strides=1,
dilation_rate=2,
activation='relu'))
enc.add(
Conv1D(filters=128,
kernel_size=3,
strides=1,
dilation_rate=2,
activation='relu'))
self.enc_mu = Stack()
self.enc_mu.add(enc)
self.enc_mu.add(Flatten())
self.enc_mu.add(Dense(latent_size))
self.enc_std = Stack()
self.enc_std.add(enc)
self.enc_std.add(Flatten())
self.enc_std.add(Dense(latent_size, activation='softplus'))
self.dec = Stack()
self.dec.add(Dense(1000))
self.dec.add(Reshape((100, 10)))
self.dec.add(
Conv1DTranspose(filters=128,
kernel_size=3,
strides=1,
dilation_rate=2,
activation='relu'))
self.dec.add(
Conv1DTranspose(filters=64,
kernel_size=3,
strides=1,
dilation_rate=2,
activation='relu'))
self.dec.add(
Conv1DTranspose(filters=32,
kernel_size=3,
strides=1,
dilation_rate=2,
activation='relu'))
def call(self, inputs):
sequence = inputs['primary']
embedded = self.input_embedding(sequence)
pad_embedded = pad_up_to(embedded, (-1, self.max_seq_len, -1), 0)
pad_embedded.set_shape((None, self.max_seq_len, 128))
z_mu = self.enc_mu(pad_embedded)
z_std = self.enc_std(pad_embedded)
z = z_mu + K.random_normal(K.shape(z_std)) * z_std
encoder_output = self.dec(z)
inputs['encoder_output'] = encoder_output
return inputs
def get_optimal_batch_sizes(self):
bucket_sizes = np.array(
[100, 200, 300, 400, 600, 900, 1000, 1300, 2000, 3000])
batch_sizes = np.array([4, 4, 4, 4, 3, 3, 3, 2, 1, 0.5, 0])
batch_sizes = np.asarray(batch_sizes * self._get_gpu_memory(),
np.int32)
batch_sizes[batch_sizes <= 0] = 1
return bucket_sizes, batch_sizes
def __init__(self, n_symbols, latent_size=32, max_seq_len=10000):
self.latent_size = latent_size
self.max_seq_len = max_seq_len
super().__init__(n_symbols)
self.input_embedding = Embedding(n_symbols, 128)
enc = Stack()
enc.add(
Conv1D(filters=32,
kernel_size=7,
strides=1,
dilation_rate=2,
activation='relu'))
enc.add(
Conv1D(filters=64,
kernel_size=5,
strides=1,
dilation_rate=2,
activation='relu'))
enc.add(
Conv1D(filters=128,
kernel_size=3,
strides=1,
dilation_rate=2,
activation='relu'))
self.enc_mu = Stack()
self.enc_mu.add(enc)
self.enc_mu.add(Flatten())
self.enc_mu.add(Dense(latent_size))
self.enc_std = Stack()
self.enc_std.add(enc)
self.enc_std.add(Flatten())
self.enc_std.add(Dense(latent_size, activation='softplus'))
self.dec = Stack()
self.dec.add(Dense(1000))
self.dec.add(Reshape((100, 10)))
self.dec.add(
Conv1DTranspose(filters=128,
kernel_size=3,
strides=1,
dilation_rate=2,
activation='relu'))
self.dec.add(
Conv1DTranspose(filters=64,
kernel_size=3,
strides=1,
dilation_rate=2,
activation='relu'))
self.dec.add(
Conv1DTranspose(filters=32,
kernel_size=3,
strides=1,
dilation_rate=2,
activation='relu'))
def build_output_model(
self, layers: List[tf.keras.Model]) -> List[tf.keras.Model]:
ssa = SoftSymmetricAlignment(Stack(layers))
return [ssa, OrdinalRegression(5)]
def __init__(self,
n_symbols,
n_layers=5,
length=3000,
latent_size=1000,
n_filters=256,
kernel_size=5,
pooling_type='average',
dropout=0):
super().__init__(n_symbols)
self._n_layers = n_layers
self._length = length
self._latent_size = latent_size
self._kernel_size = kernel_size
self._n_filters = n_filters
pool = AveragePooling1D if pooling_type == 'average' else MaxPooling1D
input_embedding = Stack()
input_embedding.add(
Embedding(n_symbols, 128, input_length=self._length))
input_embedding.add(Lambda(lambda x: x * np.sqrt(n_filters)))
input_embedding.add(PositionEmbedding())
input_embedding.add(
PaddedConv(1,
n_filters,
kernel_size,
1,
activation='relu',
dropout=dropout))
encoder = Stack()
encoder.add(input_embedding)
for _ in range(6):
for _ in range(n_layers):
encoder.add(
ResidualBlock(1,
n_filters,
kernel_size,
activation='relu',
dilation_rate=1,
dropout=dropout))
encoder.add(pool(2, 2))
latent = Stack()
latent.add(Flatten())
latent.add(Dense(self._latent_size))
decoder = Stack()
decoder.add(
Dense(47 * n_filters,
input_shape=(self._latent_size, ),
activation='relu'))
decoder.add(Reshape((47, n_filters)))
for _ in range(6):
decoder.add(UpSampling1D(2))
for _ in range(n_layers):
encoder.add(
ResidualBlock(1,
n_filters,
kernel_size,
activation='relu',
dilation_rate=1,
dropout=dropout))
decoder.add(Cropping1D((0, 8)))
self.encoder = encoder
self.decoder = decoder
self.latent = latent
def __init__(self,
n_classes: int,
input_name: str = 'encoder_output',
output_name: str = 'logits'):
super().__init__()
self._input_name = input_name
self._output_name = output_name
def max_pool_30(x):
maxpool, _ = tf.nn.top_k(x, 30)
return maxpool
conv6 = Stack(
[DeepSFConv(10, 6) for _ in range(10)] +
[Permute([2, 1]), Lambda(max_pool_30),
Flatten()])
conv10 = Stack(
[DeepSFConv(10, 10) for _ in range(10)] +
[Permute([2, 1]), Lambda(max_pool_30),
Flatten()])
output_model = Stack()
# Make conv layers
output_model.add(Concatenate(-1))
output_model.add(
Dense(500,
activation='relu',
kernel_initializer='he_normal',
kernel_constraint=tf.keras.constraints.max_norm(3)))
output_model.add(Dropout(0.2))
output_model.add(Dense(n_classes, kernel_initializer='he_normal'))
self.conv6 = conv6
self.conv10 = conv10
self.output_model = output_model
