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resunet_vggunet_model.py
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resunet_vggunet_model.py
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# -*- coding: utf-8 -*-
"""ResUNet Data Preprocessing and Model.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1fs10HVurota63WhL5vKzYLceY2GUay4Q
"""
from google.colab import drive
drive.mount('/content/drive')
import os
import numpy as np
import pandas as pd
import cv2
import seaborn as sns
import matplotlib.pyplot as plt
from skimage import io
import glob
import random
import tensorflow as tf
from sklearn.preprocessing import StandardScaler, normalize
from keras_preprocessing.image import ImageDataGenerator
from tensorflow.python.keras import Sequential
from tensorflow.keras import layers, optimizers
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
from tensorflow.keras.initializers import glorot_uniform
from tensorflow.keras.utils import plot_model
from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint, LearningRateScheduler
import tensorflow.keras.backend as K
from IPython.display import display
brain_df = pd.read_csv('/content/drive/MyDrive/ML Project/lgg-mri-segmentation/kaggle_3m/ImgAndMaskPath.csv')
brain_df
import plotly.graph_objects as go
fig = go.Figure([go.Bar(x=brain_df['mask'].value_counts().index,
y=brain_df['mask'].value_counts(),
width=[.4, .4]
)
])
fig.update_traces(marker_color='rgb(158,202,225)', marker_line_color='rgb(8,48,107)',
marker_line_width=4, opacity=0.4
)
fig.update_layout(title_text="Mask Count Plot",
width=700,
height=550,
yaxis=dict(
title_text="Count",
tickmode="array",
titlefont=dict(size=20)
)
)
fig.update_yaxes(automargin=True)
fig.show()
def visualization_grid(img_count):
count = 0
fig, axs = plt.subplots(img_count, 3, figsize = (20, 50))
for i in range(len(brain_df)):
if brain_df['mask'][i] ==1 and count <img_count:
img = io.imread(brain_df.image_path[i])
axs[count][0].title.set_text('Brain MRI')
axs[count][0].imshow(img)
mask = io.imread(brain_df.mask_path[i])
axs[count][1].title.set_text('Mask')
axs[count][1].imshow(mask, cmap = 'gray')
img[mask == 255] = (255, 0, 0)
axs[count][2].title.set_text('MRI with Mask')
axs[count][2].imshow(img)
count+=1
fig.tight_layout()
visualization_grid(6)
"""## Skip this part and goto this [cell](https://colab.research.google.com/drive/1fs10HVurota63WhL5vKzYLceY2GUay4Q?authuser=3#scrollTo=kHR4bnA99a3c&line=2&uniqifier=1) (BUILDING A SEGMENTATION MODEL TO LOCALIZE TUMOR) part
## Creating Train, Val and Test Sets and Data Augmentation
"""
brain_df_train = brain_df.drop(columns=['patient_id'])
# Convert the data in mask column to string format, to use categorical mode in flow_from_dataframe
brain_df_train['mask'] = brain_df_train['mask'].apply(lambda x: str(x))
brain_df_train.info()
from sklearn.model_selection import train_test_split
train, test = train_test_split(brain_df_train, test_size=0.15)
datagen = ImageDataGenerator(rescale=1./255., validation_split=0.1)
train_generator = datagen.flow_from_dataframe(train,
directory='./',
x_col='image_path',
y_col='mask',
subset='training',
class_mode='categorical',
batch_size=16,
shuffle=True,
target_size=(256,256)
)
valid_generator = datagen.flow_from_dataframe(train,
directory='./',
x_col='image_path',
y_col='mask',
subset='validation',
class_mode='categorical',
batch_size=16,
shuffle=True,
target_size=(256,256)
)
test_datagen = ImageDataGenerator(rescale=1./255.)
test_generator = test_datagen.flow_from_dataframe(test,
directory='./',
x_col='image_path',
y_col='mask',
class_mode='categorical',
batch_size=16,
shuffle=False,
target_size=(256,256)
)
"""## TRAIN A CLASSIFIER MODEL TO DETECT IF TUMOR EXISTS OR NOT"""
from tensorflow.keras.applications.resnet50 import ResNet50
clf_model = ResNet50(weights='imagenet', include_top=False, input_tensor=Input(shape=(256,256,3)))
clf_model.summary()
for layer in clf_model.layers:
layers.trainable = False
"""**Training the last layers of ResNet50 Fully connected layers with the brain tumor segmentation dataset**"""
head = clf_model.output
head = AveragePooling2D(pool_size=(4,4))(head)
head = Flatten(name='Flatten')(head)
head = Dense(256, activation='relu')(head)
head = Dropout(0.3)(head)
head = Dense(256, activation='relu')(head)
head = Dropout(0.3)(head)
head = Dense(2, activation='softmax')(head)
model = Model(clf_model.input, head)
model.compile(loss = 'categorical_crossentropy',
optimizer='adam',
metrics= ["accuracy"]
)
model.summary()
earlystopping = EarlyStopping(monitor='val_loss',
mode='min',
verbose=1,
patience=15
)
checkpointer = ModelCheckpoint(filepath="clf-resnet-weights.hdf5",
verbose=1,
save_best_only=True
)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
mode='min',
verbose=1,
patience=10,
min_delta=0.0001,
factor=0.2
)
callbacks = [checkpointer, earlystopping, reduce_lr]
with tf.device("/GPU:0"):
h = model.fit(train_generator,
steps_per_epoch= train_generator.n // train_generator.batch_size,
epochs = 20,
validation_data= valid_generator,
validation_steps= valid_generator.n // valid_generator.batch_size,
callbacks=[checkpointer, earlystopping])
# Saving the ResNet Trained Model
model.save('/content/drive/MyDrive/ML Project/models/ResNet_Trained_model.h5')
# Saving model achitecture in json file
model_json = model.to_json()
with open("clf-resnet-model.json", "w") as json_file:
json_file.write(model_json)
h.history.keys()
plt.figure(figsize=(12,5))
plt.subplot(1,2,1)
plt.plot(h.history['loss']);
plt.plot(h.history['val_loss']);
plt.title("Classification Model LOSS");
plt.ylabel("loss");
plt.xlabel("Epochs");
plt.legend(['train', 'val']);
plt.subplot(1,2,2)
plt.plot(h.history['accuracy']);
plt.plot(h.history['val_accuracy']);
plt.title("Classification Model Acc");
plt.ylabel("Accuracy");
plt.xlabel("Epochs");
plt.legend(['train', 'val']);
_, acc = model.evaluate(test_generator)
print("Test accuracy : {} %".format(acc*100))
prediction = model.predict(test_generator)
pred = np.argmax(prediction, axis=1)
#pred = np.asarray(pred).astype('str')
original = np.asarray(test['mask']).astype('int')
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
accuracy = accuracy_score(original, pred)
print(accuracy)
cm = confusion_matrix(original, pred)
report = classification_report(original, pred, labels = [0,1])
print(report)
plt.figure(figsize = (5,5))
sns.heatmap(cm, annot=True);
"""## BUILDING A SEGMENTATION MODEL TO LOCALIZE TUMOR"""
brain_df_mask = brain_df[brain_df['mask'] == 1]
brain_df_mask.shape
# creating test, train and val sets
from sklearn.model_selection import train_test_split
X_train, X_val = train_test_split(brain_df_mask, test_size=0.15)
X_test, X_val = train_test_split(X_val, test_size=0.5)
print("Train size is {}, valid size is {} & test size is {}".format(len(X_train), len(X_val), len(X_test)))
train_ids = list(X_train.image_path)
train_mask = list(X_train.mask_path)
val_ids = list(X_val.image_path)
val_mask= list(X_val.mask_path)
class DataGenerator(tf.keras.utils.Sequence):
def __init__(self, ids , mask, image_dir = './', batch_size = 16, img_h = 256, img_w = 256, shuffle = True):
self.ids = ids
self.mask = mask
self.image_dir = image_dir
self.batch_size = batch_size
self.img_h = img_h
self.img_w = img_w
self.shuffle = shuffle
self.on_epoch_end()
def __len__(self):
'Get the number of batches per epoch'
return int(np.floor(len(self.ids)) / self.batch_size)
def __getitem__(self, index):
'Generate a batch of data'
#generate index of batch_size length
indexes = self.indexes[index* self.batch_size : (index+1) * self.batch_size]
#get the ImageId corresponding to the indexes created above based on batch size
list_ids = [self.ids[i] for i in indexes]
#get the MaskId corresponding to the indexes created above based on batch size
list_mask = [self.mask[i] for i in indexes]
#generate data for the X(features) and y(label)
X, y = self.__data_generation(list_ids, list_mask)
#returning the data
return X, y
def on_epoch_end(self):
'Used for updating the indices after each epoch, once at the beginning as well as at the end of each epoch'
#getting the array of indices based on the input dataframe
self.indexes = np.arange(len(self.ids))
#if shuffle is true, shuffle the indices
if self.shuffle:
np.random.shuffle(self.indexes)
def __data_generation(self, list_ids, list_mask):
'generate the data corresponding the indexes in a given batch of images'
# create empty arrays of shape (batch_size,height,width,depth)
#Depth is 3 for input and depth is taken as 1 for output becasue mask consist only of 1 channel.
X = np.empty((self.batch_size, self.img_h, self.img_w, 3))
y = np.empty((self.batch_size, self.img_h, self.img_w, 1))
#iterate through the dataframe rows, whose size is equal to the batch_size
for i in range(len(list_ids)):
#path of the image
img_path = str(list_ids[i])
#mask path
mask_path = str(list_mask[i])
#reading the original image and the corresponding mask image
img = io.imread(img_path)
mask = io.imread(mask_path)
#resizing and coverting them to array of type float64
img = cv2.resize(img,(self.img_h,self.img_w))
img = np.array(img, dtype = np.float64)
mask = cv2.resize(mask,(self.img_h,self.img_w))
mask = np.array(mask, dtype = np.float64)
#standardising
img -= img.mean()
img /= img.std()
mask -= mask.mean()
mask /= mask.std()
#Adding image to the empty array
X[i,] = img
#expanding the dimnesion of the image from (256,256) to (256,256,1)
y[i,] = np.expand_dims(mask, axis = 2)
#normalizing y
y = (y > 0).astype(int)
return X, y
train_data = DataGenerator(train_ids, train_mask)
val_data = DataGenerator(val_ids, val_mask)
"""### Implemeting ResUNet Architecture Model"""
def resblock(X, f):
'''
function for creating res block
'''
X_copy = X #copy of input
# main path
X = Conv2D(f, kernel_size=(1,1), kernel_initializer='he_normal')(X)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(f, kernel_size=(3,3), padding='same', kernel_initializer='he_normal')(X)
X = BatchNormalization()(X)
# shortcut path
X_copy = Conv2D(f, kernel_size=(1,1), kernel_initializer='he_normal')(X_copy)
X_copy = BatchNormalization()(X_copy)
# Adding the output from main path and short path together
X = Add()([X, X_copy])
X = Activation('relu')(X)
return X
def upsample_concat(x, skip):
'''
funtion for upsampling image
'''
X = UpSampling2D((2,2))(x)
merge = Concatenate()([X, skip])
return merge
def ResUNet(input_width, input_height):
X_input = Input((input_height, input_width,3)) #iniating tensor of input shape
# Stage 1
conv_1 = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(X_input)
conv_1 = BatchNormalization()(conv_1)
conv_1 = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv_1)
conv_1 = BatchNormalization()(conv_1)
pool_1 = MaxPool2D((2,2))(conv_1)
# stage 2
conv_2 = resblock(pool_1, 32)
pool_2 = MaxPool2D((2,2))(conv_2)
# Stage 3
conv_3 = resblock(pool_2, 64)
pool_3 = MaxPool2D((2,2))(conv_3)
# Stage 4
conv_4 = resblock(pool_3, 128)
pool_4 = MaxPool2D((2,2))(conv_4)
# Stage 5 (bottle neck)
conv_5 = resblock(pool_4, 256)
# Upsample Stage 1
up_1 = upsample_concat(conv_5, conv_4)
up_1 = resblock(up_1, 128)
# Upsample Stage 2
up_2 = upsample_concat(up_1, conv_3)
up_2 = resblock(up_2, 64)
# Upsample Stage 3
up_3 = upsample_concat(up_2, conv_2)
up_3 = resblock(up_3, 32)
# Upsample Stage 4
up_4 = upsample_concat(up_3, conv_1)
up_4 = resblock(up_4, 16)
# final output
out = Conv2D(1, (1,1), kernel_initializer='he_normal', padding='same', activation='sigmoid')(up_4)
seg_model = Model(X_input, out)
return seg_model
res_model = ResUNet(256,256)
res_model.summary()
"""## Training Segmentation Model"""
# Define a custom loss function for ResUNet model
from keras.losses import binary_crossentropy
epsilon = 1e-5
smooth = 1
def tversky(y_true, y_pred):
y_true_pos = K.flatten(y_true)
y_pred_pos = K.flatten(y_pred)
true_pos = K.sum(y_true_pos * y_pred_pos)
false_neg = K.sum(y_true_pos * (1-y_pred_pos))
false_pos = K.sum((1-y_true_pos)*y_pred_pos)
alpha = 0.7
return (true_pos + smooth)/(true_pos + alpha*false_neg + (1-alpha)*false_pos + smooth)
def focal_tversky(y_true,y_pred):
y_true = tf.cast(y_true, tf.float32)
y_pred = tf.cast(y_pred, tf.float32)
pt_1 = tversky(y_true, y_pred)
gamma = 0.75
return K.pow((1-pt_1), gamma)
def tversky_loss(y_true, y_pred):
return 1 - tversky(y_true,y_pred)
# compling model and callbacks functions
adam = tf.keras.optimizers.Adam(lr = 0.05, epsilon = 0.1)
seg_model.compile(optimizer = adam,
loss = focal_tversky,
metrics = [tversky]
)
#callbacks
earlystopping = EarlyStopping(monitor='val_loss',
mode='min',
verbose=1,
patience=20
)
# save the best model with lower validation loss
checkpointer = ModelCheckpoint(filepath="ResUNet1-segModel-weights.hdf5",
verbose=1,
save_best_only=True
)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
mode='min',
verbose=1,
patience=10,
min_delta=0.0001,
factor=0.2
)
with tf.device("/GPU:0"):
h = seg_model.fit(train_data,
epochs = 60,
validation_data = val_data,
callbacks = [checkpointer, earlystopping, reduce_lr]
)
# Saving the ResNet Trained Model
seg_model.save('/content/drive/MyDrive/ML Project/models/ResUNet1_Segmen_model.h5')
# Saving seg_model achitecture in json file
seg_model_json = seg_model.to_json()
with open("ResUNet1-seg-model.json", "w") as json_file:
json_file.write(seg_model_json)
from tensorflow.keras.models import load_model
seg_model.load_weights('/content/drive/MyDrive/ML Project/models/ResUNet-segModel-weights.hdf5')
"""### SEGMENTATION MODEL EVALUATION"""
h.history.keys()
def plot_segmentation_loss_w_tversky(hist, b_seg_model):
plt.figure(figsize=(12,5))
plt.subplot(1,2,1)
plt.plot(h.history['loss'])
plt.plot(h.history['val_loss'])
plt.title("SEG Model focal tversky Loss")
plt.ylabel("focal tversky loss")
plt.xlabel("Epochs")
plt.legend(['train', 'val'])
plt.subplot(1,2,2)
plt.plot(h.history['tversky'])
plt.plot(h.history['val_tversky'])
plt.title("SEG Model tversky score")
plt.ylabel("tversky Accuracy")
plt.xlabel("Epochs")
plt.legend(['train', 'val'])
test_ids = list(X_test.image_path)
test_mask = list(X_test.mask_path)
test_data = DataGenerator(test_ids, test_mask)
_, tv = b_seg_model.evaluate(test_data)
print("Segmentation tversky is {:.2f}%".format(tv*100))
"""### SEGMENTATION MODEL PREFORMACE, COMBINING CLASSIFICATION AND SEGMENTAION MODEL BUILDING PIPELINE"""
from tensorflow.keras.models import load_model
model = load_model('/content/drive/MyDrive/ML Project/models/ResNet_Trained_model.h5')
def prediction(test, model, model_seg):
'''
Predcition function which takes dataframe containing ImageID as Input and perform 2 type of prediction on the image
Initially, image is passed through the classification network which predicts whether the image has defect or not, if the model
is 99% sure that the image has no defect, then the image is labeled as no-defect, if the model is not sure, it passes the image to the
segmentation network, it again checks if the image has defect or not, if it has defect, then the type and location of defect is found
'''
# empty list to store results
mask, image_id, has_mask = [], [], []
#itetrating through each image in test data
for i in test.image_path:
img = io.imread(i)
#normalizing
img = img *1./255.
#reshaping
img = cv2.resize(img, (256,256))
# converting img into array
img = np.array(img, dtype=np.float64)
#reshaping the image from 256,256,3 to 1,256,256,3
img = np.reshape(img, (1,256,256,3))
#making prediction for tumor in image
is_defect = model.predict(img)
#if tumour is not present we append the details of the image to the list
if np.argmax(is_defect)==0:
image_id.append(i)
has_mask.append(0)
mask.append('No mask :)')
continue
#Creating a empty array of shape 1,256,256,1
X = np.empty((1,256,256,3))
# read the image
img = io.imread(i)
#resizing the image and coverting them to array of type float64
img = cv2.resize(img, (256,256))
img = np.array(img, dtype=np.float64)
# standardising the image
img -= img.mean()
img /= img.std()
#converting the shape of image from 256,256,3 to 1,256,256,3
X[0,] = img
#make prediction of mask
predict = model_seg.predict(X)
# if sum of predicted mask is 0 then there is not tumour
if predict.round().astype(int).sum()==0:
image_id.append(i)
has_mask.append(0)
mask.append('No mask :)')
else:
#if the sum of pixel values are more than 0, then there is tumour
image_id.append(i)
has_mask.append(1)
mask.append(predict)
return pd.DataFrame({'image_path': image_id,'predicted_mask': mask,'has_mask': has_mask})
# making prediction
def prediction_with_seg_vis(model, b_seg_model, model_hist):
df_pred = prediction(X_test, model, b_seg_model)
df_pred = X_test.merge(df_pred, on='image_path')
plot_segmentation_loss_w_tversky(model_hist, b_seg_model)
correct_count = df_pred['mask']==df_pred['has_mask']
out = correct_count.value_counts()
print("Prediction Accuracy is {:.5f}%".format(out[1]/sum(out)*100))
#visualizing prediction
count = 0
fig, axs = plt.subplots(5,5, figsize=(30,40))
for i in range(len(df_pred)):
if df_pred.has_mask[i]==1 and count<5:
#read mri images
img = io.imread(df_pred.image_path[i])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
axs[count][0].imshow(img)
axs[count][0].title.set_text('Brain MRI')
#read original mask
mask = io.imread(df_pred.mask_path[i])
axs[count][1].imshow(mask)
axs[count][1].title.set_text('Original Mask')
#read predicted mask
pred = np.array(df_pred.predicted_mask[i]).squeeze().round()
axs[count][2].imshow(pred)
axs[count][2].title.set_text('AI predicted mask')
#overlay original mask with MRI
img[mask==255] = (255,0,0)
axs[count][3].imshow(img)
axs[count][3].title.set_text('Brain MRI with original mask (Ground Truth)')
#overlay predicted mask and MRI
img_ = io.imread(df_pred.image_path[i])
img_ = cv2.cvtColor(img_, cv2.COLOR_BGR2RGB)
img_[pred==1] = (0,255,150)
axs[count][4].imshow(img_)
axs[count][4].title.set_text('MRI with AI PREDICTED MASK')
count +=1
if (count==15):
break
fig.tight_layout()
prediction_with_seg_vis(model, seg_model, h)
"""![u-net-architecture.png](data:image/png;base64,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)
## **VGGUNET Architecture**
"""
def vggUnet (input_h, input_w, channel_first=True):
## Input shape channel last architecture
img_input = Input(shape=(input_h, input_w,3))
x = Conv2D(64, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(img_input)
x = Conv2D(64, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(x)
x = BatchNormalization()(x)
f1 = x
x = MaxPooling2D((2,2), strides=(2,2), name='block1_pool1')(x)
#Block 2
x = Conv2D(128, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(x)
x = Conv2D(128, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(x)
x = BatchNormalization()(x)
f2 = x
x = MaxPooling2D((2,2), strides=(2,2))(x)
#Block 3
x = Conv2D(256, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(x)
x = Conv2D(256, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(x)
x = Conv2D(256, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(x)
x = BatchNormalization()(x)
f3 = x
x = MaxPooling2D((2,2), strides=(2,2))(x)
#Block 4
x = Conv2D(512, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(x)
x = Conv2D(512, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(x)
x = Conv2D(512, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(x)
x = BatchNormalization()(x)
f4 = x
x = MaxPooling2D((2,2), strides=(2,2))(x)
#Block 4
x = Conv2D(512, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(x)
x = Conv2D(512, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(x)
x = Conv2D(512, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(x)
x = BatchNormalization()(x)
f5 = x
u = f5
u = (ZeroPadding2D((1,1)))(u)
u = (Conv2D(512, (3,3), activation='relu', padding='valid'))(u)
u = (BatchNormalization())(u)
u = (UpSampling2D((2,2)))(u)
# first concat
u = (concatenate([u,f4]))
#Next Upsampling
u = (ZeroPadding2D((1,1)))(u)
u = (Conv2D(256, (3,3), activation='relu'))(u)
u = (BatchNormalization())(u)
u = (UpSampling2D((2,2)))(u)
# first concat
u = (concatenate([u,f3]))
# Next upsampling
u = ZeroPadding2D((1,1))(u)
u = Conv2D(128, (3,3), activation='relu')(u)
u = BatchNormalization()(u)
u = UpSampling2D((2,2))(u)
# first concat
u = concatenate([u,f2])
# #Next Upsampling
u = ZeroPadding2D((1,1))(u)
u = Conv2D(64, (3,3), activation='relu')(u)
u = BatchNormalization()(u)
u = UpSampling2D((2,2))(u)
# first concat
u = concatenate([u,f1])
u = Conv2D(1,(1,1), activation='sigmoid', kernel_initializer='he_normal')(u)
model = Model(img_input, u)
return model
vgg_model = vggUnet(256,256)
vgg_model.summary()
adam = tf.keras.optimizers.Adam(lr = 0.05, epsilon = 0.1)
vgg_model.compile(optimizer = adam,
loss = focal_tversky,
metrics = [tversky]
)
#callbacks
earlystopping = EarlyStopping(monitor='val_loss',
mode='min',
verbose=1,
patience=20
)
# save the best model with lower validation loss
checkpointer_vgg = ModelCheckpoint(filepath="VGGUNet1-segModel-weights.hdf5",
verbose=1,
save_best_only=True
)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
mode='min',
verbose=1,
patience=10,
min_delta=0.0001,
factor=0.2
)
with tf.device("/GPU:0"):
vgg_ = vgg_model.fit(train_data,
epochs = 100,
validation_data = val_data,
callbacks = [earlystopping,checkpointer_vgg, reduce_lr]
)
# making prediction
prediction_with_seg_vis(model, vgg_model, vgg_)
from keras.utils.vis_utils import plot_model
plot_model(vgg_model, to_file='/content/drive/MyDrive/ML Project/models/vggUnet.png', show_shapes=True)
vgg_model.save('/content/drive/MyDrive/ML Project/models/VggUnet_Segmen_model.h5')
vgg_model.save_weights('/content/drive/MyDrive/ML Project/models/VggUnet_Segmen_model_weights.h5')
plot_model(seg_model, to_file='/content/drive/MyDrive/ML Project/models/resUnet.png', show_shapes=True)