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unet_tools.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 25 13:08:55 2020
Unet models
@author: amt
"""
import tensorflow as tf
import numpy as np
from scipy import signal
def make_large_unet(fac, sr, ncomps=3):
# BUILD THE MODEL
# These models start with an input
if ncomps==1:
input_layer=tf.keras.layers.Input(shape=(64,2)) # 1 Channel seismic data
elif ncomps==3:
input_layer=tf.keras.layers.Input(shape=(64,6)) # 1 Channel seismic data
# First block
level1=tf.keras.layers.Conv1D(int(fac*32),21,activation='relu',padding='same')(input_layer) # N filters, Filter Size, Stride, padding
network=tf.keras.layers.MaxPooling1D()(level1) #32
# Second Block
level2=tf.keras.layers.Conv1D(int(fac*64),15,activation='relu',padding='same')(network)
network=tf.keras.layers.MaxPooling1D()(level2) #16
#network=tf.keras.layers.ZeroPadding1D((0,1))(network)
#Next Block
level3=tf.keras.layers.Conv1D(int(fac*128),11,activation='relu',padding='same')(network)
#network=tf.keras.layers.BatchNormalization()(level3)
network=tf.keras.layers.MaxPooling1D()(level3) #8
#Base of Network
network=tf.keras.layers.Flatten()(network)
base_level=tf.keras.layers.Dense(8,activation='relu')(network)
#network=tf.keras.layers.BatchNormalization()(base_level)
network=tf.keras.layers.Reshape((8,1))(base_level)
#Upsample and add skip connections
network=tf.keras.layers.Conv1D(int(fac*128),11,activation='relu',padding='same')(network) # N filters, Filter Size, Stride, padding
network=tf.keras.layers.UpSampling1D()(network)
level3=tf.keras.layers.Concatenate()([network,level3]) # N filters, Filter Size, Stride, padding
# level3=tf.keras.layers.Lambda( lambda x: x[:,:-1,:])(level3)
#Upsample and add skip connections
network=tf.keras.layers.Conv1D(int(fac*64),15,activation='relu',padding='same')(level3) # N filters, Filter Size, Stride, padding
network=tf.keras.layers.UpSampling1D()(network)
level2=tf.keras.layers.Concatenate()([network,level2]) # N filters, Filter Size, Stride, padding
#Upsample and add skip connections
network=tf.keras.layers.Conv1D(int(fac*32),21,activation='relu',padding='same')(level2) # N filters, Filter Size, Stride, padding
network=tf.keras.layers.UpSampling1D()(network)
level1=tf.keras.layers.Concatenate()([network,level1]) # N filters, Filter Size, Stride, padding
#End of network
network=tf.keras.layers.Conv1D(1,21,activation='sigmoid',padding='same')(level1) # N filters, Filter Size, Stride, padding
output=tf.keras.layers.Flatten()(network) # N filters, Filter Size, Stride, padding
model=tf.keras.models.Model(input_layer,output)
opt = tf.keras.optimizers.Adam(lr=0.0001)
model.compile(loss='binary_crossentropy',optimizer=opt,metrics=['accuracy'])
model.summary()
return model
def make_large_unet_drop(fac,sr,ncomps=1):
if ncomps==1:
input_layer=tf.keras.layers.Input(shape=(64,2)) # 1 Channel seismic data
elif ncomps==3:
input_layer=tf.keras.layers.Input(shape=(64,6)) # 1 Channel seismic data
# First block
level1=tf.keras.layers.Conv1D(int(fac*32),21,activation='relu',padding='same')(input_layer) # N filters, Filter Size, Stride, padding
network=tf.keras.layers.MaxPooling1D()(level1) #32
# Second Block
level2=tf.keras.layers.Conv1D(int(fac*64),15,activation='relu',padding='same')(network)
network=tf.keras.layers.MaxPooling1D()(level2) #16
#network=tf.keras.layers.ZeroPadding1D((0,1))(network)
#Next Block
level3=tf.keras.layers.Conv1D(int(fac*128),11,activation='relu',padding='same')(network)
#network=tf.keras.layers.BatchNormalization()(level3)
network=tf.keras.layers.MaxPooling1D()(level3) #8
#Base of Network
network=tf.keras.layers.Flatten()(network)
base_level=tf.keras.layers.Dense(8,activation='relu')(network)
#network=tf.keras.layers.BatchNormalization()(base_level)
network=tf.keras.layers.Reshape((8,1))(base_level)
#Upsample and add skip connections
network=tf.keras.layers.Conv1D(int(fac*128),11,activation='relu',padding='same')(network) # N filters, Filter Size, Stride, padding
network=tf.keras.layers.UpSampling1D()(network)
level3=tf.keras.layers.Concatenate()([network,level3]) # N filters, Filter Size, Stride, padding
#level3=tf.keras.layers.Lambda( lambda x: x[:,:-1,:])(level3)
#Upsample and add skip connections
network=tf.keras.layers.Conv1D(int(fac*64),15,activation='relu',padding='same')(level3) # N filters, Filter Size, Stride, padding
network=tf.keras.layers.UpSampling1D()(network)
level2=tf.keras.layers.Concatenate()([network,level2]) # N filters, Filter Size, Stride, padding
#Upsample and add skip connections
network=tf.keras.layers.Conv1D(int(fac*32),21,activation='relu',padding='same')(level2) # N filters, Filter Size, Stride, padding
network=tf.keras.layers.UpSampling1D()(network)
level1=tf.keras.layers.Concatenate()([network,level1]) # N filters, Filter Size, Stride, padding
#End of network
network=tf.keras.layers.Dropout(.2)(level1)
network=tf.keras.layers.Conv1D(1,21,activation='sigmoid',padding='same')(level1) # N filters, Filter Size, Stride, padding
output=tf.keras.layers.Flatten()(network) # N filters, Filter Size, Stride, padding
model=tf.keras.models.Model(input_layer,output)
opt = tf.keras.optimizers.Adam(lr=0.0001)
model.compile(loss='binary_crossentropy',optimizer=opt,metrics=['accuracy'])
model.summary()
return model
def my_3comp_data_generator(batch_size,x_data,n_data,sig_inds,noise_inds,sr,std,valid=False,nlen=128):
epsilon=1e-6
while True:
# randomly select a starting index for the data batch
start_of_data_batch=np.random.choice(len(sig_inds)-batch_size//2)
# randomly select a starting index for the noise batch
start_of_noise_batch=np.random.choice(len(noise_inds)-batch_size//2)
if valid:
start_of_noise_batch=0
start_of_data_batch=0
# get range of indicies from data
datainds=sig_inds[start_of_data_batch:start_of_data_batch+batch_size//2]
# get range of indicies from noise
noiseinds=noise_inds[start_of_noise_batch:start_of_noise_batch+batch_size//2]
# grab batch
comp1=np.concatenate((x_data[datainds,:nlen],n_data[noiseinds,:nlen]))
comp2=np.concatenate((x_data[datainds,nlen:2*nlen],n_data[noiseinds,nlen:2*nlen]))
comp3=np.concatenate((x_data[datainds,2*nlen:],n_data[noiseinds,2*nlen:]))
# make target data vector for batch
target=np.concatenate((np.ones_like(datainds),np.zeros_like(noiseinds)))
# make structure to hold target functions
batch_target=np.zeros((batch_size,nlen))
# shuffle things (not sure if this is needed)
inds=np.arange(batch_size)
np.random.shuffle(inds)
comp1=comp1[inds,:]
comp2=comp2[inds,:]
comp3=comp3[inds,:]
target=target[inds]
# some params
winsize=64 # winsize in seconds
# this just makes a nonzero value where the pick is
for ii, targ in enumerate(target):
#print(ii,targ)
if targ==0:
batch_target[ii,:]=np.zeros((1,nlen))
elif targ==1:
batch_target[ii,:]=signal.gaussian(nlen,std=int(std*sr))
# I have 30 s of data and want to have 15 s windows in which the arrival can occur anywhere
time_offset=np.random.uniform(0,winsize,size=batch_size)
# initialize arrays to hold shifted data
new_batch=np.zeros((batch_size,int(winsize*sr),3))
new_batch_target=np.zeros((batch_size,int(winsize*sr)))
# this loop shifts data and targets and stores results
for ii,offset in enumerate(time_offset):
bin_offset=int(offset*sr) #HZ sampling Frequency
start_bin=bin_offset
end_bin=start_bin+int(winsize*sr)
new_batch[ii,:,0]=comp1[ii,start_bin:end_bin]
new_batch[ii,:,1]=comp2[ii,start_bin:end_bin]
new_batch[ii,:,2]=comp3[ii,start_bin:end_bin]
new_batch_target[ii,:]=batch_target[ii,start_bin:end_bin]
# does feature log
new_batch_sign=np.sign(new_batch)
new_batch_val=np.log(np.abs(new_batch)+epsilon)
batch_out=[]
for ii in range(new_batch_target.shape[0]):
batch_out.append(np.hstack( [new_batch_val[ii,:,0].reshape(-1,1), new_batch_sign[ii,:,0].reshape(-1,1),
new_batch_val[ii,:,1].reshape(-1,1), new_batch_sign[ii,:,1].reshape(-1,1),
new_batch_val[ii,:,2].reshape(-1,1), new_batch_sign[ii,:,2].reshape(-1,1)] ) )
batch_out=np.array(batch_out)
yield(batch_out,new_batch_target)