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make_and_run_model.py
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make_and_run_model.py
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import keras
from keras.models import Sequential, load_model
from keras.layers import Input, Add, Multiply, Dense, MaxPooling3D, BatchNormalization, Reshape
from keras.layers.convolutional import Conv1D, Conv2D, Conv3D, Convolution2D
from keras.preprocessing.image import ImageDataGenerator
from keras.layers.convolutional import ZeroPadding3D, ZeroPadding2D, ZeroPadding1D, UpSampling2D
from keras.layers.core import Dropout
from keras.utils import to_categorical
from keras.layers import LeakyReLU, MaxPooling2D, concatenate,Conv2DTranspose, Concatenate, ZeroPadding2D
from keras.activations import relu
from keras.callbacks import History, ModelCheckpoint
import numpy as np
from predict import save_image
#from custom_loss import *
#from models.neurotech_models import *
from math import sqrt
from utils import *
import json
def make_unet( image_dim, nlabels, activation_hidden, activation_output, verbose=0):
img_rows=image_dim[1]
img_cols=image_dim[2]
nMLP=16
nRshp=int(sqrt(nMLP))
nUpSm=int(image_dim[0]/nRshp)
image = Input(shape=(image_dim[1], image_dim[2],1))
n_downsample=4
#if x != 0 or y != 0 :
# print("Error: image must have dimensions that can be divided by "+"2^"+str(n_downsample)+" but has dimensions "+str(image_dim[1])+","+str(image_dim[1]) )
# exit(1)
BN1 = BatchNormalization()(image)
conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(BN1)
conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(pool1)
conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(pool2)
conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(pool3)
conv4 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Convolution2D(512, 3, 3, activation='relu', border_mode='same')(pool4)
conv5 = Convolution2D(512, 3, 3, activation='relu', border_mode='same')(conv5)
up5 = UpSampling2D(size=(2, 2))(conv5)
#up5 = Conv2DTranspose( filters=512, kernel_size=(3,3), strides=(2, 2), padding='same')(conv5)
conc5 = Concatenate(axis=3)([up5, conv4])
conv6 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(conc5)
conv6 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(conv6)
up6 = UpSampling2D(size=(2, 2))(conv6)
#up6 = Conv2DTranspose( filters=512, kernel_size=(3,3), strides=(2, 2), padding='same')(conv6)
conc6 = Concatenate(axis=3)([up6, conv3])
conv7 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(up6)
conv7 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(conv7)
up7 = UpSampling2D(size=(2, 2))(conv7)
#up7 = Conv2DTranspose( filters=512, kernel_size=(3,3), strides=(2, 2), padding='same')(conv7)
conc7 = Concatenate(axis=3)([up7, conv2])
conv8 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conc7) #(up8)
conv8 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv8)
up8 = Conv2DTranspose( filters=512, kernel_size=(3,3), strides=(2, 2), padding='same')(conv8)
conc8 = Concatenate(axis=3)([up8, conv1])
conv9 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conc8) #(up9)
conv9 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv9)
conv10 = Convolution2D(nlabels, 1, 1, activation=activation_output)(conv9)
model = keras.models.Model(input=[image], output=conv10)
if verbose > 0 :
print(model.summary())
return model
def make_dil( image_dim):
image = Input(shape=(image_dim[1], image_dim[2],1))
OUT = BatchNormalization()(image)
#kDim=[3,3,3,3,3,3,3]
#nK=[21,21,21,21,21,22,21,1]
n_dil=[1,2,4,8,16,1]
#n_dil=[1,1,1,2,2,4,4,8,16,1,1]
n_layers=int(len(n_dil))
kDim=[6] * n_layers
nK=[26] * n_layers
for i in range(n_layers):
OUT = Conv2D( nK[i] , kernel_size=[kDim[i],kDim[i]], dilation_rate=(n_dil[i],n_dil[i]),activation='relu',padding='same')(OUT)
OUT = BatchNormalization()(OUT)
OUT = Dropout(0.25)(OUT)
OUT = Conv2D(1, kernel_size=1, padding='same', activation='sigmoid')(OUT)
model = keras.models.Model(inputs=[image], outputs=OUT)
return(model)
def base_model( image_dim, nlabels, nK, n_dil, kernel_size, drop_out, activation_hidden, activation_output, verbose=1):
print("N Labels:", nlabels)
print("Drop out:",drop_out)
print("Number of Dilations:", n_dil)
print("Activation hidden:", activation_hidden)
print("Activation output:", activation_output)
nK=[int(i) for i in nK.split(",") ]
if n_dil == None :
n_dil=[1] * len(nK)
else:
n_dil=[int(i) for i in n_dil.split(",") ]
IN = CONV = Input(shape=(image_dim[1], image_dim[2],1))
n_layers=int(len(nK))
kDim=[kernel_size] * n_layers
for i in range(n_layers):
print("Layer:", i, nK[i], kDim[i], n_dil[i])
CONV = Conv2D(nK[i], kernel_size=[kDim[i],kDim[i]],dilation_rate=(n_dil[i],n_dil[i]), activation=activation_hidden,padding='same')(CONV)
CONV = Dropout(drop_out)(CONV)
OUT = Conv2D(nlabels, kernel_size=[1,1], activation=activation_output, padding='same')(CONV)
model = keras.models.Model(inputs=[IN], outputs=OUT)
if verbose > 0 :
print(model.summary())
return(model)
def make_model( image_dim, nlabels,nK, n_dil, kernel_size, drop_out, model_type='model_0_0', activation_hidden="relu", activation_output="sigmoid", verbose=0):
if model_type=='unet' :
model=make_unet( image_dim, nlabels, activation_hidden, activation_output)
#elif model_type=='dil': model=make_dil( image_dim, nlabels, activation_hidden, activation_output)
#elif model_type=='model_0_0': model=model_0_0( image_dim, nlabels, nK, kernel_size, drop_out, activation_hidden, activation_output)
#elif model_type=='model_1_0': model=model_1_0( image_dim, nlabels, nK, kernel_size, drop_out, activation_hidden, activation_output)
#elif model_type=='model_1_1': model=model_1_1( image_dim, nlabels, nK, kernel_size, drop_out, activation_hidden, activation_output)
#elif model_type=='model_2_0': model=model_2_0( image_dim, nlabels, nK, kernel_size, drop_out, activation_hidden, activation_output)
#elif model_type=='model_2_1': model=model_2_1( image_dim, nlabels, nK, kernel_size, drop_out, activation_hidden, activation_output)
#elif model_type=='model_3_0': model=model_3_0( image_dim, nlabels, nK, kernel_size, drop_out, activation_hidden, activation_output)
#elif model_type=='model_3_1': model=model_3_1( image_dim, nlabels, nK, kernel_size, drop_out, activation_hidden, activation_output)
#elif model_type=='model_4_0': model=model_4_0( image_dim, nlabels, nK, kernel_size, drop_out, activation_hidden, activation_output)
#elif model_type=='model_4_1': model=model_4_1( image_dim, nlabels, nK, kernel_size, drop_out, activation_hidden, activation_output)
else :
model=base_model( image_dim, nlabels, nK, n_dil, kernel_size, drop_out, activation_hidden, activation_output)
return(model)
def compile_and_run(model, model_name, history_fn, X_train, Y_train, X_validate, Y_validate, nb_epoch, nlabels, metric="categorical_accuracy", loss='categorical_crossentropy', lr=0.005, verbose=0):
#set compiler
ada = keras.optimizers.Adam(0.0001)
#set checkpoint filename
checkpoint_fn = splitext(model_name)[0]+"_checkpoint-{epoch:02d}-{val_loss:.2f}.hdf5"
#create checkpoint callback for model
checkpoint = ModelCheckpoint(checkpoint_fn, monitor='val_loss', verbose=0, save_best_only=True, mode='max')
#compile the model
model.compile(loss = loss, optimizer=ada,metrics=[metric] )
#fit model
X_train = X_train
X_validate = X_validate
if loss in categorical_functions :
Y_train = to_categorical(Y_train, num_classes=nlabels)
Y_validate = to_categorical(Y_validate, num_classes=nlabels)
history = model.fit([X_train],Y_train, validation_data=([X_validate], Y_validate), epochs = nb_epoch,callbacks=[ checkpoint])
#save model
model.save(model_name)
with open(history_fn, 'w+') as fp: json.dump(history.history, fp)
return([model, history])