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tcn_seq_train_used.py
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tcn_seq_train_used.py
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from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten,MultiHeadAttention
from tensorflow import keras
import tensorflow as tf
import numpy as np
from tcn import TCN
import wandb
from wandb.keras import WandbCallback
import pickle
import os
wandb.init(project="mass2smiles-tcn_seq")
batch_size=16
n_epochs=50
ytr=np.load('/home/delser/train/tcn/cddd_all_HRMS_train_24012023_cddd_refine.npy')
#ytr=np.expand_dims(ytr,-1)
ytr1=np.load('/home/delser/train/tcn/y1_all_HRMS_train_24012023_cddd_mf.npy')
yval=np.load('/home/delser/train/tcn/cddd_all_HRMS_valid_24012023_cddd_refine.npy')
yval1=np.load('/home/delser/train/tcn/y1_all_HRMS_valid_24012023_cddd_mf.npy')
#yval=np.expand_dims(yval,-1)
xtr=np.load('/home/delser/train/tcn/tcn_train_seq_sin256_2401.npy',mmap_mode='r')
xval =np.load( '/home/delser/train/tcn/tcn_valid_seq_sin256_2401.npy',mmap_mode='r')
class BaseAttention(tf.keras.layers.Layer):
def __init__(self, **kwargs):
super().__init__()
self.mha = tf.keras.layers.MultiHeadAttention(**kwargs)
self.layernorm = tf.keras.layers.LayerNormalization()
self.add = tf.keras.layers.Add()
class FeedForward(tf.keras.layers.Layer):
def __init__(self, d_model, dff, dropout_rate=0.1):
super().__init__()
self.seq = tf.keras.Sequential([
tf.keras.layers.Dense(dff, activation='relu'),
tf.keras.layers.Dense(d_model),
tf.keras.layers.Dropout(dropout_rate)
])
self.add = tf.keras.layers.Add()
self.layer_norm = tf.keras.layers.LayerNormalization()
def call(self, x):
x = self.add([x, self.seq(x)])
x = self.layer_norm(x)
return x
class GlobalSelfAttention(BaseAttention):
def call(self, x):
attn_output = self.mha(
query=x,
value=x,
key=x)
x = self.add([x, attn_output])
x = self.layernorm(x)
return x
class EncoderLayer(tf.keras.layers.Layer):
def __init__(self,*, d_model, num_heads, dff, dropout_rate=0.1):
super().__init__()
self.self_attention = GlobalSelfAttention(
num_heads=num_heads,
key_dim=d_model,
dropout=dropout_rate)
self.ffn = FeedForward(d_model, dff)
def call(self, x):
x = self.self_attention(x)
x = self.ffn(x)
return x
class DataGenerator(keras.utils.Sequence):
def __init__(self, x_data, y_data,y1_data, batch_size):
self.x, self.y, self.y1 = x_data, y_data, y1_data,
self.batch_size = batch_size
self.num_batches = np.ceil(len(x_data) / batch_size)
self.batch_idx = np.array_split(range(len(x_data)), self.num_batches)
def __len__(self):
return len(self.batch_idx)
def __getitem__(self, idx):
batch_x = self.x[self.batch_idx[idx]]
batch_y = self.y[self.batch_idx[idx]]
batch_y1 = self.y1[self.batch_idx[idx]]
return batch_x, [batch_y,batch_y1]
train_generator = DataGenerator(xtr, ytr,ytr1, batch_size = 16)
def call_existing_code(units, heads, dropout,dense_dropout,lr,filters,num_layers):
tcn=TCN(
nb_filters=filters,
kernel_size=8,
dilations=[2 ** i for i in range(6)],
use_skip_connections=True,
use_layer_norm=True,
kernel_initializer='glorot_uniform',
go_backwards=True,)
print(f'TCN.receptive_field: {tcn.receptive_field}.')
input0=tf.keras.Input(shape=(501,257))
input1=tf.keras.layers.Masking(mask_value=10,input_shape=(501,257))(input0)
att = Sequential([
EncoderLayer(d_model=257, num_heads=heads, dff=units,dropout_rate=dropout)
for _ in range(num_layers)])(input1)
hd_tcn=tcn(att)
output_b=Sequential([
Dropout(rate=dense_dropout),
Dense(128, activation='tanh'),
Dropout(rate=dense_dropout),
Dense(71, activation='sigmoid')],name="funct_groups")(hd_tcn)
output_a = Sequential([
Dropout(rate=dense_dropout),
Dense(512, activation='relu'),
Dropout(rate=dense_dropout),
Dense(512, activation='linear')
],name="smiles")(hd_tcn)
model= tf.keras.Model(inputs=input0, outputs=[output_a,output_b])
model.compile(loss={"smiles":'mean_absolute_error',"funct_groups":'mse'}, optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),metrics={"smiles":'mean_squared_error',"funct_groups":'mean_absolute_error'})
return model
def build_model():
num_layers = 5
units = 2048
heads = 16
dropout = 0.1
dense_dropout = 0.1
filters=256
#activation = hp.Choice("activation", ["relu", "tanh"])
#dropout = hp.Boolean("dropout")
#lr = hp.Float("lr", min_value=1e-6, max_value=1e-4, sampling="log")
# call existing model-building code with the hyperparameter values.
#model = call_existing_code(units=units, heads=heads, dropout=dropout,dense_dropout=dense_dropout,lr=lr,filters=filters)
model = call_existing_code(units=units, heads=heads, dropout=dropout,dense_dropout=dense_dropout,lr=1e-4,filters=filters, num_layers =num_layers)
return model
# Compile and train.
model= build_model()
model.summary()
model.fit(train_generator,validation_data=(xval,[yval,yval1]), epochs=n_epochs,shuffle=False,batch_size=None,validation_batch_size=16,callbacks=[WandbCallback(log_batch_frequency=1)])
model.save_weights('/home/delser/train/tcn/model')
del model
model= build_model()
model.load_weights('/home/delser/train/tcn/model')
result= model.predict(xval)
#np.save("/home/delser/train/tcn/val_predict.npy", result)
np.save("/home/delser/train/tcn/val_predict.npy", result[0])
np.save("/home/delser/train/tcn/val_predict1.npy", result[1])
print('done!')