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cnn.py
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#!/usr/bin/env python3
import random
import os
import sys
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Dropout, ReLU, Flatten, Activation, Conv2D, MaxPooling2D, Reshape, \
UpSampling2D, GaussianNoise, Dense, Rescaling
from tensorflow.keras.layers import concatenate, add
from tensorflow.keras.models import Model
import tensorflow_addons as tfa
import json
from custom_layers import HiCScale, CombineConcat, ClipByValue
from utils import IMAGE_SIZE, get_split_imageset
from metrics import compute_auc
def cnn_run(chroms, run_id, seed, dataset_name, epoch=50):
dataset_dir = os.path.join('dataset', dataset_name)
# seed = hash(run_id)
print('#' * 10 + ' Start training U-Net ' + '#' * 10)
train_images, train_y, val_images, val_y, test_images, test_y = \
get_split_imageset(dataset_dir, IMAGE_SIZE, seed, chroms)
image_upper_bound = np.quantile(train_images, 0.996)
CNN_METRICS = [
tf.keras.metrics.BinaryAccuracy(name='binary_accuracy', threshold=0.5),
tf.keras.metrics.AUC(curve="ROC", name='ROC_AUC'),
tf.keras.metrics.AUC(curve="PR", name='PR_AUC')
]
cnn_learning_rate = tf.keras.optimizers.schedules.PolynomialDecay(
0.001,
2000 * 20,
end_learning_rate=0.00005,
power=2.0
)
# Data preparation (convert to tensors)
train_images_tensor = tf.convert_to_tensor(train_images, dtype=tf.float32)
val_images_tensor = tf.convert_to_tensor(val_images, dtype=tf.float32)
train_x_tensors = [train_images_tensor]
val_x_tensors = [val_images_tensor]
flatten_train_y = train_y.reshape((-1, IMAGE_SIZE * IMAGE_SIZE))[..., np.newaxis]
flatten_val_y = val_y.reshape((-1, IMAGE_SIZE * IMAGE_SIZE))[..., np.newaxis]
# Batch size setup
bs = 8
I = Input(shape=(IMAGE_SIZE, IMAGE_SIZE))
# x = HiCScale(image_upper_bound)(I)
x = ClipByValue(image_upper_bound)(I)
x = Rescaling(1 / image_upper_bound)(x)
x = Reshape((IMAGE_SIZE, IMAGE_SIZE, 1))(x)
x = GaussianNoise(0.05)(x)
conv1 = Conv2D(32, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(x)
conv1 = Conv2D(32, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(pool1)
conv2 = Conv2D(64, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(conv2)
drop2 = Dropout(0.3)(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(drop2)
conv3 = Conv2D(128, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
pool2)
conv3 = Conv2D(128, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
conv3)
drop3 = Dropout(0.3)(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(drop3)
conv4 = Conv2D(256, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
pool3)
conv4 = Conv2D(256, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(512, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
pool4)
conv5 = Conv2D(512, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(256, 2, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
UpSampling2D(size=(2, 2))(drop5))
merge6 = concatenate([drop4, up6], axis=3)
conv6 = Conv2D(256, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
merge6)
conv6 = Conv2D(256, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
conv6)
up7 = Conv2D(128, 2, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
UpSampling2D(size=(2, 2))(conv6))
merge7 = concatenate([conv3, up7], axis=3)
conv7 = Conv2D(128, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
merge7)
conv7 = Conv2D(128, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
conv7)
up8 = Conv2D(64, 2, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
UpSampling2D(size=(2, 2))(conv7))
merge8 = concatenate([conv2, up8], axis=3)
conv8 = Conv2D(64, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
merge8)
conv8 = Conv2D(64, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(conv8)
up9 = Conv2D(32, 2, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
UpSampling2D(size=(2, 2))(conv8))
merge9 = concatenate([conv1, up9], axis=3)
conv9 = Conv2D(32, 3, activation='relu', padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(
merge9)
conv9 = Conv2D(16, 3, padding='same', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(conv9)
conv10 = conv9
image_embedding = Reshape((IMAGE_SIZE * IMAGE_SIZE, -1), name='cnn_embedding')(conv10)
image_decode = ReLU()(image_embedding)
image_decode = Dropout(0.3)(image_decode)
image_decode = Dense(32, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(image_decode)
image_decode = Dropout(0.3)(image_decode)
image_decode = Dense(16, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(image_decode)
image_decode = Dropout(0.3)(image_decode)
image_decode = Dense(8, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(image_decode)
image_decode = Dropout(0.3)(image_decode)
cnn_logits = Dense(1, name='cnn_logits', kernel_regularizer=tf.keras.regularizers.l2(0.0001))(image_decode)
cnn_sigmoid = Activation('sigmoid', name='cnn_sigmoid')(cnn_logits)
CNN = Model(inputs=[I], outputs=[cnn_logits, cnn_sigmoid])
CNN.compile(
loss={
'cnn_sigmoid': tfa.losses.SigmoidFocalCrossEntropy(from_logits=False, alpha=0.5, gamma=1.2,
reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE)
},
loss_weights={'cnn_sigmoid': IMAGE_SIZE * IMAGE_SIZE},
optimizer=tf.keras.optimizers.Adam(learning_rate=cnn_learning_rate),
metrics={
'cnn_sigmoid': CNN_METRICS
}
)
inputs = [train_x_tensors[0]]
history = CNN.fit(
inputs, y=[flatten_train_y, flatten_train_y],
batch_size=bs, epochs=epoch,
validation_data=([val_x_tensors[0]], [flatten_val_y, flatten_val_y]),
callbacks=[
tf.keras.callbacks.EarlyStopping(
monitor='val_cnn_sigmoid_' + 'PR_AUC', # use validation AUC of precision-recall for stopping
min_delta=0.0001, patience=5,
verbose=1, mode='max'),
],
verbose=2
)
train_y_pred = np.asarray(CNN.predict([train_x_tensors[0]])[1])
val_y_pred = np.asarray(CNN.predict([val_x_tensors[0]])[1])
test_y_pred = np.asarray(CNN.predict([test_images])[1])
train_auc, train_ap = compute_auc(train_y_pred, train_y.astype('bool'))
val_auc, val_ap = compute_auc(val_y_pred, val_y.astype('bool'))
test_auc, test_ap = compute_auc(test_y_pred, test_y.astype('bool'))
print('=' * 30)
print('*******CNN**********')
print('Train AUC is {}. Train AP is {}.'.format(train_auc, train_ap))
print('Validation AUC is {}. Validation AP is {}.'.format(val_auc, val_ap))
print('Test AUC is {}. Test AP is {}.'.format(test_auc, test_ap))
CNN.save(os.path.join('models', '{}_CNN'.format(run_id)))