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dense2DkernelCNN.py
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dense2DkernelCNN.py
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from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Flatten, Conv2DTranspose, Reshape, Activation, Concatenate, Lambda
from tensorflow.keras.models import Model
from tensorflow.keras import backend as K
from tensorflow.keras.utils import plot_model
import numpy as np
import json
from telescope import telescopeMSE2
import tensorflow as tf
##Need to use 32 bits for telescopeMSE
tf.keras.backend.set_floatx('float32')
from denseCNN import denseCNN
class dense2DkernelCNN(denseCNN):
def __init__(self,name='',weights_f=''):
self.name=name
self.pams ={
'CNN_layer_nodes' : [8], #n_filters
'CNN_kernel_size' : [3],
'CNN_padding' : ['same'],
'CNN_pool' : [False],
'share_filters' : True,
'Dense_layer_nodes': [], #does not include encoded layer
'encoded_dim' : 12,
'shape' : (4,4,3),
'channels_first' : False,
'arrange' : [],
'arrMask' : [],
'calQMask' : [],
'n_copy' : 0, # no. of copy for hi occ datasets
'loss' : '',
'optimizer' : 'adam',
}
self.weights_f =weights_f
def init(self,printSummary=True):
encoded_dim = self.pams['encoded_dim']
CNN_layer_nodes = self.pams['CNN_layer_nodes']
CNN_kernel_size = self.pams['CNN_kernel_size']
CNN_padding = self.pams['CNN_padding']
CNN_pool = self.pams['CNN_pool']
Dense_layer_nodes = self.pams['Dense_layer_nodes'] #does not include encoded layer
channels_first = self.pams['channels_first']
share_filters = self.pams['share_filters']
# fix to one cnn layer for now
nnodes =CNN_layer_nodes[0] #8
CNN_kernel =CNN_kernel_size[0] #3
CNN_padding=CNN_padding[0]
inputs = Input(shape=self.pams['shape'], name='input_1')
x = inputs
x1 = Lambda(lambda x: x[:,:,:,0:1], name='lambda_1')(x)
x2 = Lambda(lambda x: x[:,:,:,1:2], name='lambda_2')(x)
x3 = Lambda(lambda x: x[:,:,:,2:3], name='lambda_3')(x)
if share_filters:
conv = Conv2D(nnodes, CNN_kernel, activation='relu',padding=CNN_padding, name='conv2d_1')
x1 = conv(x1)
x2 = conv(x2)
x3 = conv(x3)
else:
x1 = Conv2D(nnodes, CNN_kernel, activation='relu',padding=CNN_padding)(x1)
x2 = Conv2D(nnodes, CNN_kernel, activation='relu',padding=CNN_padding)(x2)
x3 = Conv2D(nnodes, CNN_kernel, activation='relu',padding=CNN_padding)(x3)
if CNN_pool[0]:
x1 = MaxPooling2D( (2,2), padding='same')(x1)
x2 = MaxPooling2D( (2,2), padding='same')(x2)
x3 = MaxPooling2D( (2,2), padding='same')(x3)
conv_vol_slice = K.int_shape(x1)
x1 = Flatten(name='flatten_1')(x1)
x2 = Flatten(name='flatten_2')(x2)
x3 = Flatten(name='flatten_3')(x3)
x = [x1,x2,x3]
x = Concatenate(axis=-1,name='concat_1')(x)
conv_vol = K.int_shape(x)
encodedLayer = Dense(encoded_dim, activation='relu',name='encoded_vector')(x)
# Instantiate Encoder Model
self.encoder = Model(inputs, encodedLayer, name='encoder')
if printSummary:
self.encoder.summary()
encoded_inputs = Input(shape=(encoded_dim,), name='decoder_input')
x = encoded_inputs
x = Dense(conv_vol[1], activation='relu',name='dense_2')(x)
x = Reshape((conv_vol_slice[1],conv_vol_slice[2],nnodes,3,),name='reshape_1')(x)
x1 = Lambda(lambda x: x[:,:,:,:,0], name='lambda_4')(x)
x2 = Lambda(lambda x: x[:,:,:,:,1], name='lambda_5')(x)
x3 = Lambda(lambda x: x[:,:,:,:,2], name='lambda_6')(x)
if CNN_pool[0]:
x1 = UpSampling2D( (2,2) )(x1)
x2 = UpSampling2D( (2,2) )(x2)
x3 = UpSampling2D( (2,2) )(x3)
## Use n filter here
conv_t = Conv2DTranspose(nnodes, CNN_kernel, activation='relu', padding=CNN_padding, name='conv2d_transpose_1')
x1 = conv_t(x1)
x2 = conv_t(x2)
x3 = conv_t(x3)
## Always use 1 filter
conv_t2 = Conv2DTranspose(1, CNN_kernel, activation=None, padding='same', name='conv2d_transpose_2')
x1 = conv_t2(x1)
x2 = conv_t2(x2)
x3 = conv_t2(x3)
x = [x1,x2,x3]
x = Concatenate(axis=-1,name='concat_2')(x)
outputs = Activation('sigmoid', name='decoder_output')(x)
self.decoder = Model(encoded_inputs, outputs, name='decoder')
if printSummary:
self.decoder.summary()
self.autoencoder = Model(inputs, self.decoder(self.encoder(inputs)), name='autoencoder')
if printSummary:
self.autoencoder.summary()
self.compileModels()
CNN_layers=''
if len(CNN_layer_nodes)>0:
CNN_layers += '_Conv'
for i,n in enumerate(CNN_layer_nodes):
CNN_layers += f'_{n}x{CNN_kernel_size[i]}'
if CNN_pool[i]:
CNN_layers += 'pooled'
Dense_layers = ''
if len(Dense_layer_nodes)>0:
Dense_layers += '_Dense'
for n in Dense_layer_nodes:
Dense_layers += f'_{n}'
self.name = f'Autoencoded{CNN_layers}{Dense_layers}_Encoded_{encoded_dim}'
if not self.weights_f=='':
self.autoencoder.load_weights(self.weights_f)