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In [1]:
import os
os.environ['THEANO_FLAGS']='mode=FAST_RUN,device=gpu,floatX=float32'In [2]:
import skimage
from skimage import transform, color
from matplotlib import pyplot as plt
import numpy as np
# import pandas as pd
# import scipy as sp
import seaborn as sns
%matplotlib inline
# import h5py
# import shapely
# from shapely import geometry, affinity
from path import Path
# import json
import arrow
from tqdm import tqdm
import keras
from keras.preprocessing import imageUsing Theano backend. Using gpu device 0: GeForce GTX 860M (CNMeM is disabled, cuDNN 4007)
In [3]:
plt.rcParams['figure.figsize']=(10,10)In [4]:
from keras.models import Model
from keras.layers import Input, merge, Convolution2D, MaxPooling2D, UpSampling2D
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as KIn [7]:
from keras.datasets import mnist
from keras.preprocessing.image import ImageDataGenerator
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
X_train = X_train.astype('float32')
data_gen_args=dict(horizontal_flip=True,vertical_flip=True,rotation_range=90.,width_shift_range=0.2,
height_shift_range=0.2, zoom_range=0.2,)
datagen1 = ImageDataGenerator(**data_gen_args)
datagen2 = ImageDataGenerator(**data_gen_args)
seed=1
batch_size=3
gen1=datagen1.flow(X_train, y_train, batch_size=batch_size ,seed=seed, shuffle=True)
gen2=datagen2.flow(X_train, y_train, batch_size=batch_size, seed=seed, shuffle=True)
for i in range(10):
X_train1, y_train1=next(gen1)
X_train2, y_train2=next(gen2)
for b in range(batch_size-1):
assert (X_train1[b]==X_train2[b]).all()
assert (y_train1[b]==y_train2[b]).all()
print('✓ ImageDataGenerator is repeatable')✓ ImageDataGenerator is repeatable
In [8]:
img_rows = 80
img_cols = 112
batch_size=10
output_shape=(img_rows,img_cols)In [12]:
dest_dir = Path('./data/augumented/train')
dest_dir_test = Path('./data/augumented/test')
# make sure image match
images=sorted(dest_dir.glob('image/*.png'))
masks=sorted(dest_dir.glob('mask/*.png'))
assert len(dest_dir.glob('image/*.png'))==len(dest_dir.glob('mask/*.png')), 'should be same number of pngs'
for i,[image,mask] in enumerate(zip(images,masks)):
assert image.basename()==mask.basename(),'i=%s %s!=%s'%(i,image.basename(),mask.basename())In [13]:
data_gen_args=dict(
rotation_range=10.,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=np.deg2rad(10),
zoom_range=0.1,
channel_shift_range=0.01,
fill_mode='constant',
horizontal_flip=True,
vertical_flip=True,
rescale=1/255.
)
datagen1 = ImageDataGenerator(**data_gen_args)
datagen2 = ImageDataGenerator(**data_gen_args)
image_gen=datagen1.flow_from_directory(dest_dir,
class_mode=None,
classes=['image'],
batch_size=batch_size,
seed=seed,
target_size=output_shape,
)
mask_gen=datagen2.flow_from_directory(dest_dir,
class_mode=None,
classes=['mask'],
batch_size=batch_size,
seed=seed,
target_size=output_shape,
)
# join the generators (converting the mask to greyscale)
def dual_gen(image_gen,mask_gen):
for image,mask in zip(image_gen,mask_gen):
mask=skimage.color.rgb2grey(np.transpose(mask,(0,2,3,1)))
yield image,mask
train_gen=dual_gen(image_gen,mask_gen)
X_train, y_train=next(train_gen)
X_train.shape, y_train.shapeOut [13]:
Found 3870 images belonging to 1 classes. Found 3870 images belonging to 1 classes.
((10, 3, 80, 112), (10, 80, 112))
In [14]:
# test gen
data_gen_args=dict(
fill_mode='constant',
rescale=1/255.
)
datagen_test1 = ImageDataGenerator(**data_gen_args)
datagen_test2 = ImageDataGenerator(**data_gen_args)
image_gen_test=datagen_test1.flow_from_directory(dest_dir_test,
class_mode=None,
classes=['image'],
batch_size=batch_size,
seed=seed,
target_size=output_shape,
)
mask_gen_test=datagen_test2.flow_from_directory(dest_dir_test,
class_mode=None,
classes=['mask'],
batch_size=batch_size,
seed=seed,
target_size=output_shape,
)
# join the generators
def dual_gen(image_gen,mask_gen):
for image,mask in zip(image_gen,mask_gen):
mask=skimage.color.rgb2grey(np.transpose(mask,(0,2,3,1)))
yield image,mask
test_gen=dual_gen(image_gen_test,mask_gen_test)
X_test, y_test=next(test_gen)
X_test.shape, y_test.shapeOut [14]:
Found 503 images belonging to 1 classes. Found 503 images belonging to 1 classes.
((10, 3, 80, 112), (10, 80, 112))
In [63]:
# train gen, but this time un-augumented so I can directly compare them for overfitting
data_gen_args=dict(
fill_mode='constant',
rescale=1/255.
)
datagen_test1b = ImageDataGenerator(**data_gen_args)
datagen_test2b = ImageDataGenerator(**data_gen_args)
image_gen_train2=datagen_test1b.flow_from_directory(dest_dir,
class_mode=None,
classes=['image'],
batch_size=batch_size,
seed=seed,
target_size=output_shape,
)
mask_gen_train2=datagen_test2b.flow_from_directory(dest_dir,
class_mode=None,
classes=['mask'],
batch_size=batch_size,
seed=seed,
target_size=output_shape,
)
# join the generators
def dual_gen(image_gen,mask_gen):
for image,mask in zip(image_gen,mask_gen):
mask=skimage.color.rgb2grey(np.transpose(mask,(0,2,3,1)))
yield image,mask
train_gen_unaugumented=dual_gen(image_gen_train2,mask_gen_train2)
X_test, y_test=next(train_gen_unaugumented)
X_test.shape, y_test.shapeOut [63]:
Found 3870 images belonging to 1 classes. Found 3870 images belonging to 1 classes.
((10, 3, 80, 112), (10, 80, 112))
In [16]:
# View some of the data
seed=1
n=5
rows=n
cols=4
pltnb=0
plt.figure(figsize=(15,rows*2))
for i in range(n):
X_train, y_train=next(train_gen)
for b in range(2):
# create a grid of 3x2
pltnb+=1
plt.subplot(rows,cols,pltnb)
plt.title('i=%s batchn=%s datagen1'%(i,b))
plt.imshow(np.transpose(X_train[b],(1,2,0)))
plt.colorbar()
plt.axis('off')
pltnb+=1
plt.subplot(rows,cols,pltnb)
plt.title('i=%s batchn=%s gen2'%(i,b))
plt.imshow(y_train[b], cmap=plt.get_cmap('gray'))
plt.colorbar()
plt.axis('off')
plt.tight_layout()
plt.show()
In [203]:
# show data dist
plt.figure(figsize=(15,5))
plt.subplot(1,2,1)
sns.distplot(X_train.flatten())
plt.title('X')
plt.subplot(1,2,2)
sns.distplot(y_train.flatten())
plt.title('y')Out [203]:
<matplotlib.text.Text at 0x7fb864bca0f0>
In [38]:
# define custom loss and metric functions
from keras import backend as K
smooth = 1
def dice_coef(y_true, y_pred, smooth=1):
"""
Dice = 2*sum(|A*B|)/(sum(A^2)+sum(B^2))
ref: https://arxiv.org/pdf/1606.04797v1.pdf
"""
intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
return (2. * intersection + smooth) / (K.sum(K.square(y_true),-1) + K.sum(K.square(y_pred),-1) + smooth)
# I think the missing one was a mistake, because it made loss(y_true,y_true)=-1
def dice_coef_loss(y_true, y_pred):
return 1-dice_coef(y_true, y_pred)
Model
The model architecture. Each box is a inception module or convolution with the number of feature layers denotes in brackets. The output size is denoted below the box and the arrows denote differen't operation.
The inception module used in this paper, as originally proposed in 1 .
-
https://arxiv.org/pdf/1512.00567v3.pdf "Rethinking the Inception Architecture for Computer Vision" ↩︎
In [45]:
import sys
from keras.models import Model
from keras.layers import Input, merge, Convolution2D, MaxPooling2D, UpSampling2D, Dense
from keras.layers import BatchNormalization, Dropout, Flatten, Lambda, Reshape
from keras.layers.advanced_activations import ELU, LeakyReLU
from keras import backend as K
def unet_inception_model(optimiser, img_cols=512, img_rows=512, main_act=LeakyReLU, dropout=0.5):
def inception_block(inputs, depth, batch_mode=0, splitted=False, activation='relu'):
"""Inception block v1 with asymetric convolutions"""
assert depth % 16 == 0
actv = activation == 'relu' and (lambda: LeakyReLU(0.0)) or activation == 'elu' and (lambda: ELU(1.0)) or None
c1_1 = Convolution2D(int(depth/4), 1, 1, init='he_normal', border_mode='same')(inputs)
c2_1 = Convolution2D(int(depth/8*3), 1, 1, init='he_normal', border_mode='same')(inputs)
c2_1 = actv()(c2_1)
if splitted:
c2_2 = Convolution2D(int(depth/2), 1, 3, init='he_normal', border_mode='same')(c2_1)
c2_2 = BatchNormalization(mode=batch_mode, axis=1)(c2_2)
c2_2 = actv()(c2_2)
c2_3 = Convolution2D(int(depth/2), 3, 1, init='he_normal', border_mode='same')(c2_2)
else:
c2_3 = Convolution2D(int(depth/2), 3, 3, init='he_normal', border_mode='same')(c2_1)
c3_1 = Convolution2D(int(depth/16), 1, 1, init='he_normal', border_mode='same')(inputs)
#missed batch norm
c3_1 = actv()(c3_1)
if splitted:
c3_2 = Convolution2D(int(depth/8), 1, 5, init='he_normal', border_mode='same')(c3_1)
c3_2 = BatchNormalization(mode=batch_mode, axis=1)(c3_2)
c3_2 = actv()(c3_2)
c3_3 = Convolution2D(int(depth/8), 5, 1, init='he_normal', border_mode='same')(c3_2)
else:
c3_3 = Convolution2D(int(depth/8), 5, 5, init='he_normal', border_mode='same')(c3_1)
p4_1 = MaxPooling2D(pool_size=(3,3), strides=(1,1), border_mode='same')(inputs)
c4_2 = Convolution2D(int(depth/8), 1, 1, init='he_normal', border_mode='same')(p4_1)
res = merge([c1_1, c2_3, c3_3, c4_2], mode='concat', concat_axis=1)
res = BatchNormalization(mode=batch_mode, axis=1)(res)
res = actv()(res)
return res
def residual_skip(inputs, num, depth, scale=0.1):
"""
A skip connection with a branch to a residual block
/ 1x1conv \
input ---------- - output
"""
residual = Convolution2D(depth, num, num, border_mode='same')(inputs)
residual = BatchNormalization(mode=2, axis=1)(residual)
residual = Lambda(lambda x: x*scale)(residual)
res = merge([inputs, residual], mode="sum")
# res = _shortcut(inputs, residual)
return main_act()(res)
def reduction_block(nb_filter, nb_row, nb_col, border_mode='same', subsample=(1, 1)):
"""Downsampling using a strided convolution followed by batchnorm and activation"""
def f(_input):
conv = Convolution2D(nb_filter=nb_filter, nb_row=nb_row, nb_col=nb_col, subsample=subsample,
border_mode=border_mode)(_input)
norm = BatchNormalization(mode=2, axis=1)(conv)
return main_act()(norm)
return f
def get_unet_inception_2head(optimizer):
splitted = True
act = 'relu'
inputs = Input((3, img_rows, img_cols), name='main_input')
conv1 = inception_block(inputs, 32, batch_mode=2, splitted=splitted, activation=act)
pool1 = reduction_block(32, 3, 3, border_mode='same', subsample=(2,2))(conv1)
pool1 = Dropout(dropout)(pool1)
conv2 = inception_block(pool1, 64, batch_mode=2, splitted=splitted, activation=act)
pool2 = reduction_block(64, 3, 3, border_mode='same', subsample=(2,2))(conv2)
pool2 = Dropout(dropout)(pool2)
conv3 = inception_block(pool2, 128, batch_mode=2, splitted=splitted, activation=act)
pool3 = reduction_block(128, 3, 3, border_mode='same', subsample=(2,2))(conv3)
pool3 = Dropout(dropout)(pool3)
conv4 = inception_block(pool3, 256, batch_mode=2, splitted=splitted, activation=act)
pool4 = reduction_block(256, 3, 3, border_mode='same', subsample=(2,2))(conv4)
pool4 = Dropout(dropout)(pool4)
conv5 = inception_block(pool4, 512, batch_mode=2, splitted=splitted, activation=act)
conv5 = Dropout(dropout)(conv5)
after_conv4 = residual_skip(conv4, 1, 256)
up6 = merge([UpSampling2D(size=(2, 2))(conv5), after_conv4], mode='concat', concat_axis=1)
conv6 = inception_block(up6, 256, batch_mode=2, splitted=splitted, activation=act)
conv6 = Dropout(dropout)(conv6)
after_conv3 = residual_skip(conv3, 1, 128)
up7 = merge([UpSampling2D(size=(2, 2))(conv6), after_conv3], mode='concat', concat_axis=1)
conv7 = inception_block(up7, 128, batch_mode=2, splitted=splitted, activation=act)
conv7 = Dropout(dropout)(conv7)
after_conv2 = residual_skip(conv2, 1, 64)
up8 = merge([UpSampling2D(size=(2, 2))(conv7), after_conv2], mode='concat', concat_axis=1)
conv8 = inception_block(up8, 64, batch_mode=2, splitted=splitted, activation=act)
conv8 = Dropout(dropout)(conv8)
after_conv1 = residual_skip(conv1, 1, 32)
up9 = merge([UpSampling2D(size=(2, 2))(conv8), after_conv1], mode='concat', concat_axis=1)
conv9 = inception_block(up9, 32, batch_mode=2, splitted=splitted, activation=act)
conv9 = Dropout(dropout)(conv9)
conv10 = Convolution2D(1, 1, 1, init='he_normal', activation='hard_sigmoid')(conv9)
reshp = Reshape((img_rows,img_cols), name='main_output')(conv10)
model = Model(input=inputs, output=reshp)
model.compile(optimizer=optimizer,
loss=[dice_coef_loss],
metrics=['accuracy']
)
return model
return get_unet_inception_2head(optimiser)
In [21]:
model_name='unet_inception_inv2'
optimizer = keras.optimizers.Nadam(lr=2e-4)
model = unet_inception_model(optimizer,img_cols,img_rows,LeakyReLU,dropout=0.5)
model_checkpoint = ModelCheckpoint('models/%s_weights.hdf5'%model_name, monitor='val_acc', save_best_only=True, save_weights_only=True)
early_stopping = keras.callbacks.EarlyStopping(patience=2, monitor='val_acc')In [22]:
model.summary()____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
main_input (InputLayer) (None, 3, 80, 112) 0
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D) (None, 12, 80, 112) 48 main_input[0][0]
____________________________________________________________________________________________________
convolution2d_5 (Convolution2D) (None, 2, 80, 112) 8 main_input[0][0]
____________________________________________________________________________________________________
leakyrelu_1 (LeakyReLU) (None, 12, 80, 112) 0 convolution2d_2[0][0]
____________________________________________________________________________________________________
leakyrelu_3 (LeakyReLU) (None, 2, 80, 112) 0 convolution2d_5[0][0]
____________________________________________________________________________________________________
convolution2d_3 (Convolution2D) (None, 16, 80, 112) 592 leakyrelu_1[0][0]
____________________________________________________________________________________________________
convolution2d_6 (Convolution2D) (None, 4, 80, 112) 44 leakyrelu_3[0][0]
____________________________________________________________________________________________________
batchnormalization_1 (BatchNormal(None, 16, 80, 112) 32 convolution2d_3[0][0]
____________________________________________________________________________________________________
batchnormalization_2 (BatchNormal(None, 4, 80, 112) 8 convolution2d_6[0][0]
____________________________________________________________________________________________________
leakyrelu_2 (LeakyReLU) (None, 16, 80, 112) 0 batchnormalization_1[0][0]
____________________________________________________________________________________________________
leakyrelu_4 (LeakyReLU) (None, 4, 80, 112) 0 batchnormalization_2[0][0]
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D) (None, 3, 80, 112) 0 main_input[0][0]
____________________________________________________________________________________________________
convolution2d_1 (Convolution2D) (None, 8, 80, 112) 32 main_input[0][0]
____________________________________________________________________________________________________
convolution2d_4 (Convolution2D) (None, 16, 80, 112) 784 leakyrelu_2[0][0]
____________________________________________________________________________________________________
convolution2d_7 (Convolution2D) (None, 4, 80, 112) 84 leakyrelu_4[0][0]
____________________________________________________________________________________________________
convolution2d_8 (Convolution2D) (None, 4, 80, 112) 16 maxpooling2d_1[0][0]
____________________________________________________________________________________________________
merge_1 (Merge) (None, 32, 80, 112) 0 convolution2d_1[0][0]
convolution2d_4[0][0]
convolution2d_7[0][0]
convolution2d_8[0][0]
____________________________________________________________________________________________________
batchnormalization_3 (BatchNormal(None, 32, 80, 112) 64 merge_1[0][0]
____________________________________________________________________________________________________
leakyrelu_5 (LeakyReLU) (None, 32, 80, 112) 0 batchnormalization_3[0][0]
____________________________________________________________________________________________________
convolution2d_9 (Convolution2D) (None, 32, 40, 56) 9248 leakyrelu_5[0][0]
____________________________________________________________________________________________________
batchnormalization_4 (BatchNormal(None, 32, 40, 56) 64 convolution2d_9[0][0]
____________________________________________________________________________________________________
leakyrelu_6 (LeakyReLU) (None, 32, 40, 56) 0 batchnormalization_4[0][0]
____________________________________________________________________________________________________
dropout_1 (Dropout) (None, 32, 40, 56) 0 leakyrelu_6[0][0]
____________________________________________________________________________________________________
convolution2d_11 (Convolution2D) (None, 24, 40, 56) 792 dropout_1[0][0]
____________________________________________________________________________________________________
convolution2d_14 (Convolution2D) (None, 4, 40, 56) 132 dropout_1[0][0]
____________________________________________________________________________________________________
leakyrelu_7 (LeakyReLU) (None, 24, 40, 56) 0 convolution2d_11[0][0]
____________________________________________________________________________________________________
leakyrelu_9 (LeakyReLU) (None, 4, 40, 56) 0 convolution2d_14[0][0]
____________________________________________________________________________________________________
convolution2d_12 (Convolution2D) (None, 32, 40, 56) 2336 leakyrelu_7[0][0]
____________________________________________________________________________________________________
convolution2d_15 (Convolution2D) (None, 8, 40, 56) 168 leakyrelu_9[0][0]
____________________________________________________________________________________________________
batchnormalization_5 (BatchNormal(None, 32, 40, 56) 64 convolution2d_12[0][0]
____________________________________________________________________________________________________
batchnormalization_6 (BatchNormal(None, 8, 40, 56) 16 convolution2d_15[0][0]
____________________________________________________________________________________________________
leakyrelu_8 (LeakyReLU) (None, 32, 40, 56) 0 batchnormalization_5[0][0]
____________________________________________________________________________________________________
leakyrelu_10 (LeakyReLU) (None, 8, 40, 56) 0 batchnormalization_6[0][0]
____________________________________________________________________________________________________
maxpooling2d_2 (MaxPooling2D) (None, 32, 40, 56) 0 dropout_1[0][0]
____________________________________________________________________________________________________
convolution2d_10 (Convolution2D) (None, 16, 40, 56) 528 dropout_1[0][0]
____________________________________________________________________________________________________
convolution2d_13 (Convolution2D) (None, 32, 40, 56) 3104 leakyrelu_8[0][0]
____________________________________________________________________________________________________
convolution2d_16 (Convolution2D) (None, 8, 40, 56) 328 leakyrelu_10[0][0]
____________________________________________________________________________________________________
convolution2d_17 (Convolution2D) (None, 8, 40, 56) 264 maxpooling2d_2[0][0]
____________________________________________________________________________________________________
merge_2 (Merge) (None, 64, 40, 56) 0 convolution2d_10[0][0]
convolution2d_13[0][0]
convolution2d_16[0][0]
convolution2d_17[0][0]
____________________________________________________________________________________________________
batchnormalization_7 (BatchNormal(None, 64, 40, 56) 128 merge_2[0][0]
____________________________________________________________________________________________________
leakyrelu_11 (LeakyReLU) (None, 64, 40, 56) 0 batchnormalization_7[0][0]
____________________________________________________________________________________________________
convolution2d_18 (Convolution2D) (None, 64, 20, 28) 36928 leakyrelu_11[0][0]
____________________________________________________________________________________________________
batchnormalization_8 (BatchNormal(None, 64, 20, 28) 128 convolution2d_18[0][0]
____________________________________________________________________________________________________
leakyrelu_12 (LeakyReLU) (None, 64, 20, 28) 0 batchnormalization_8[0][0]
____________________________________________________________________________________________________
dropout_2 (Dropout) (None, 64, 20, 28) 0 leakyrelu_12[0][0]
____________________________________________________________________________________________________
convolution2d_20 (Convolution2D) (None, 48, 20, 28) 3120 dropout_2[0][0]
____________________________________________________________________________________________________
convolution2d_23 (Convolution2D) (None, 8, 20, 28) 520 dropout_2[0][0]
____________________________________________________________________________________________________
leakyrelu_13 (LeakyReLU) (None, 48, 20, 28) 0 convolution2d_20[0][0]
____________________________________________________________________________________________________
leakyrelu_15 (LeakyReLU) (None, 8, 20, 28) 0 convolution2d_23[0][0]
____________________________________________________________________________________________________
convolution2d_21 (Convolution2D) (None, 64, 20, 28) 9280 leakyrelu_13[0][0]
____________________________________________________________________________________________________
convolution2d_24 (Convolution2D) (None, 16, 20, 28) 656 leakyrelu_15[0][0]
____________________________________________________________________________________________________
batchnormalization_9 (BatchNormal(None, 64, 20, 28) 128 convolution2d_21[0][0]
____________________________________________________________________________________________________
batchnormalization_10 (BatchNorma(None, 16, 20, 28) 32 convolution2d_24[0][0]
____________________________________________________________________________________________________
leakyrelu_14 (LeakyReLU) (None, 64, 20, 28) 0 batchnormalization_9[0][0]
____________________________________________________________________________________________________
leakyrelu_16 (LeakyReLU) (None, 16, 20, 28) 0 batchnormalization_10[0][0]
____________________________________________________________________________________________________
maxpooling2d_3 (MaxPooling2D) (None, 64, 20, 28) 0 dropout_2[0][0]
____________________________________________________________________________________________________
convolution2d_19 (Convolution2D) (None, 32, 20, 28) 2080 dropout_2[0][0]
____________________________________________________________________________________________________
convolution2d_22 (Convolution2D) (None, 64, 20, 28) 12352 leakyrelu_14[0][0]
____________________________________________________________________________________________________
convolution2d_25 (Convolution2D) (None, 16, 20, 28) 1296 leakyrelu_16[0][0]
____________________________________________________________________________________________________
convolution2d_26 (Convolution2D) (None, 16, 20, 28) 1040 maxpooling2d_3[0][0]
____________________________________________________________________________________________________
merge_3 (Merge) (None, 128, 20, 28) 0 convolution2d_19[0][0]
convolution2d_22[0][0]
convolution2d_25[0][0]
convolution2d_26[0][0]
____________________________________________________________________________________________________
batchnormalization_11 (BatchNorma(None, 128, 20, 28) 256 merge_3[0][0]
____________________________________________________________________________________________________
leakyrelu_17 (LeakyReLU) (None, 128, 20, 28) 0 batchnormalization_11[0][0]
____________________________________________________________________________________________________
convolution2d_27 (Convolution2D) (None, 128, 10, 14) 147584 leakyrelu_17[0][0]
____________________________________________________________________________________________________
batchnormalization_12 (BatchNorma(None, 128, 10, 14) 256 convolution2d_27[0][0]
____________________________________________________________________________________________________
leakyrelu_18 (LeakyReLU) (None, 128, 10, 14) 0 batchnormalization_12[0][0]
____________________________________________________________________________________________________
dropout_3 (Dropout) (None, 128, 10, 14) 0 leakyrelu_18[0][0]
____________________________________________________________________________________________________
convolution2d_29 (Convolution2D) (None, 96, 10, 14) 12384 dropout_3[0][0]
____________________________________________________________________________________________________
convolution2d_32 (Convolution2D) (None, 16, 10, 14) 2064 dropout_3[0][0]
____________________________________________________________________________________________________
leakyrelu_19 (LeakyReLU) (None, 96, 10, 14) 0 convolution2d_29[0][0]
____________________________________________________________________________________________________
leakyrelu_21 (LeakyReLU) (None, 16, 10, 14) 0 convolution2d_32[0][0]
____________________________________________________________________________________________________
convolution2d_30 (Convolution2D) (None, 128, 10, 14) 36992 leakyrelu_19[0][0]
____________________________________________________________________________________________________
convolution2d_33 (Convolution2D) (None, 32, 10, 14) 2592 leakyrelu_21[0][0]
____________________________________________________________________________________________________
batchnormalization_13 (BatchNorma(None, 128, 10, 14) 256 convolution2d_30[0][0]
____________________________________________________________________________________________________
batchnormalization_14 (BatchNorma(None, 32, 10, 14) 64 convolution2d_33[0][0]
____________________________________________________________________________________________________
leakyrelu_20 (LeakyReLU) (None, 128, 10, 14) 0 batchnormalization_13[0][0]
____________________________________________________________________________________________________
leakyrelu_22 (LeakyReLU) (None, 32, 10, 14) 0 batchnormalization_14[0][0]
____________________________________________________________________________________________________
maxpooling2d_4 (MaxPooling2D) (None, 128, 10, 14) 0 dropout_3[0][0]
____________________________________________________________________________________________________
convolution2d_28 (Convolution2D) (None, 64, 10, 14) 8256 dropout_3[0][0]
____________________________________________________________________________________________________
convolution2d_31 (Convolution2D) (None, 128, 10, 14) 49280 leakyrelu_20[0][0]
____________________________________________________________________________________________________
convolution2d_34 (Convolution2D) (None, 32, 10, 14) 5152 leakyrelu_22[0][0]
____________________________________________________________________________________________________
convolution2d_35 (Convolution2D) (None, 32, 10, 14) 4128 maxpooling2d_4[0][0]
____________________________________________________________________________________________________
merge_4 (Merge) (None, 256, 10, 14) 0 convolution2d_28[0][0]
convolution2d_31[0][0]
convolution2d_34[0][0]
convolution2d_35[0][0]
____________________________________________________________________________________________________
batchnormalization_15 (BatchNorma(None, 256, 10, 14) 512 merge_4[0][0]
____________________________________________________________________________________________________
leakyrelu_23 (LeakyReLU) (None, 256, 10, 14) 0 batchnormalization_15[0][0]
____________________________________________________________________________________________________
convolution2d_36 (Convolution2D) (None, 256, 5, 7) 590080 leakyrelu_23[0][0]
____________________________________________________________________________________________________
batchnormalization_16 (BatchNorma(None, 256, 5, 7) 512 convolution2d_36[0][0]
____________________________________________________________________________________________________
leakyrelu_24 (LeakyReLU) (None, 256, 5, 7) 0 batchnormalization_16[0][0]
____________________________________________________________________________________________________
dropout_4 (Dropout) (None, 256, 5, 7) 0 leakyrelu_24[0][0]
____________________________________________________________________________________________________
convolution2d_38 (Convolution2D) (None, 192, 5, 7) 49344 dropout_4[0][0]
____________________________________________________________________________________________________
convolution2d_41 (Convolution2D) (None, 32, 5, 7) 8224 dropout_4[0][0]
____________________________________________________________________________________________________
leakyrelu_25 (LeakyReLU) (None, 192, 5, 7) 0 convolution2d_38[0][0]
____________________________________________________________________________________________________
leakyrelu_27 (LeakyReLU) (None, 32, 5, 7) 0 convolution2d_41[0][0]
____________________________________________________________________________________________________
convolution2d_39 (Convolution2D) (None, 256, 5, 7) 147712 leakyrelu_25[0][0]
____________________________________________________________________________________________________
convolution2d_42 (Convolution2D) (None, 64, 5, 7) 10304 leakyrelu_27[0][0]
____________________________________________________________________________________________________
batchnormalization_17 (BatchNorma(None, 256, 5, 7) 512 convolution2d_39[0][0]
____________________________________________________________________________________________________
batchnormalization_18 (BatchNorma(None, 64, 5, 7) 128 convolution2d_42[0][0]
____________________________________________________________________________________________________
leakyrelu_26 (LeakyReLU) (None, 256, 5, 7) 0 batchnormalization_17[0][0]
____________________________________________________________________________________________________
leakyrelu_28 (LeakyReLU) (None, 64, 5, 7) 0 batchnormalization_18[0][0]
____________________________________________________________________________________________________
maxpooling2d_5 (MaxPooling2D) (None, 256, 5, 7) 0 dropout_4[0][0]
____________________________________________________________________________________________________
convolution2d_37 (Convolution2D) (None, 128, 5, 7) 32896 dropout_4[0][0]
____________________________________________________________________________________________________
convolution2d_40 (Convolution2D) (None, 256, 5, 7) 196864 leakyrelu_26[0][0]
____________________________________________________________________________________________________
convolution2d_43 (Convolution2D) (None, 64, 5, 7) 20544 leakyrelu_28[0][0]
____________________________________________________________________________________________________
convolution2d_44 (Convolution2D) (None, 64, 5, 7) 16448 maxpooling2d_5[0][0]
____________________________________________________________________________________________________
merge_5 (Merge) (None, 512, 5, 7) 0 convolution2d_37[0][0]
convolution2d_40[0][0]
convolution2d_43[0][0]
convolution2d_44[0][0]
____________________________________________________________________________________________________
convolution2d_45 (Convolution2D) (None, 256, 10, 14) 65792 leakyrelu_23[0][0]
____________________________________________________________________________________________________
batchnormalization_19 (BatchNorma(None, 512, 5, 7) 1024 merge_5[0][0]
____________________________________________________________________________________________________
batchnormalization_20 (BatchNorma(None, 256, 10, 14) 512 convolution2d_45[0][0]
____________________________________________________________________________________________________
leakyrelu_29 (LeakyReLU) (None, 512, 5, 7) 0 batchnormalization_19[0][0]
____________________________________________________________________________________________________
lambda_1 (Lambda) (None, 256, 10, 14) 0 batchnormalization_20[0][0]
____________________________________________________________________________________________________
dropout_5 (Dropout) (None, 512, 5, 7) 0 leakyrelu_29[0][0]
____________________________________________________________________________________________________
merge_6 (Merge) (None, 256, 10, 14) 0 leakyrelu_23[0][0]
lambda_1[0][0]
____________________________________________________________________________________________________
upsampling2d_1 (UpSampling2D) (None, 512, 10, 14) 0 dropout_5[0][0]
____________________________________________________________________________________________________
leakyrelu_30 (LeakyReLU) (None, 256, 10, 14) 0 merge_6[0][0]
____________________________________________________________________________________________________
merge_7 (Merge) (None, 768, 10, 14) 0 upsampling2d_1[0][0]
leakyrelu_30[0][0]
____________________________________________________________________________________________________
convolution2d_47 (Convolution2D) (None, 96, 10, 14) 73824 merge_7[0][0]
____________________________________________________________________________________________________
convolution2d_50 (Convolution2D) (None, 16, 10, 14) 12304 merge_7[0][0]
____________________________________________________________________________________________________
leakyrelu_31 (LeakyReLU) (None, 96, 10, 14) 0 convolution2d_47[0][0]
____________________________________________________________________________________________________
leakyrelu_33 (LeakyReLU) (None, 16, 10, 14) 0 convolution2d_50[0][0]
____________________________________________________________________________________________________
convolution2d_48 (Convolution2D) (None, 128, 10, 14) 36992 leakyrelu_31[0][0]
____________________________________________________________________________________________________
convolution2d_51 (Convolution2D) (None, 32, 10, 14) 2592 leakyrelu_33[0][0]
____________________________________________________________________________________________________
batchnormalization_21 (BatchNorma(None, 128, 10, 14) 256 convolution2d_48[0][0]
____________________________________________________________________________________________________
batchnormalization_22 (BatchNorma(None, 32, 10, 14) 64 convolution2d_51[0][0]
____________________________________________________________________________________________________
leakyrelu_32 (LeakyReLU) (None, 128, 10, 14) 0 batchnormalization_21[0][0]
____________________________________________________________________________________________________
leakyrelu_34 (LeakyReLU) (None, 32, 10, 14) 0 batchnormalization_22[0][0]
____________________________________________________________________________________________________
maxpooling2d_6 (MaxPooling2D) (None, 768, 10, 14) 0 merge_7[0][0]
____________________________________________________________________________________________________
convolution2d_46 (Convolution2D) (None, 64, 10, 14) 49216 merge_7[0][0]
____________________________________________________________________________________________________
convolution2d_49 (Convolution2D) (None, 128, 10, 14) 49280 leakyrelu_32[0][0]
____________________________________________________________________________________________________
convolution2d_52 (Convolution2D) (None, 32, 10, 14) 5152 leakyrelu_34[0][0]
____________________________________________________________________________________________________
convolution2d_53 (Convolution2D) (None, 32, 10, 14) 24608 maxpooling2d_6[0][0]
____________________________________________________________________________________________________
merge_8 (Merge) (None, 256, 10, 14) 0 convolution2d_46[0][0]
convolution2d_49[0][0]
convolution2d_52[0][0]
convolution2d_53[0][0]
____________________________________________________________________________________________________
convolution2d_54 (Convolution2D) (None, 128, 20, 28) 16512 leakyrelu_17[0][0]
____________________________________________________________________________________________________
batchnormalization_23 (BatchNorma(None, 256, 10, 14) 512 merge_8[0][0]
____________________________________________________________________________________________________
batchnormalization_24 (BatchNorma(None, 128, 20, 28) 256 convolution2d_54[0][0]
____________________________________________________________________________________________________
leakyrelu_35 (LeakyReLU) (None, 256, 10, 14) 0 batchnormalization_23[0][0]
____________________________________________________________________________________________________
lambda_2 (Lambda) (None, 128, 20, 28) 0 batchnormalization_24[0][0]
____________________________________________________________________________________________________
dropout_6 (Dropout) (None, 256, 10, 14) 0 leakyrelu_35[0][0]
____________________________________________________________________________________________________
merge_9 (Merge) (None, 128, 20, 28) 0 leakyrelu_17[0][0]
lambda_2[0][0]
____________________________________________________________________________________________________
upsampling2d_2 (UpSampling2D) (None, 256, 20, 28) 0 dropout_6[0][0]
____________________________________________________________________________________________________
leakyrelu_36 (LeakyReLU) (None, 128, 20, 28) 0 merge_9[0][0]
____________________________________________________________________________________________________
merge_10 (Merge) (None, 384, 20, 28) 0 upsampling2d_2[0][0]
leakyrelu_36[0][0]
____________________________________________________________________________________________________
convolution2d_56 (Convolution2D) (None, 48, 20, 28) 18480 merge_10[0][0]
____________________________________________________________________________________________________
convolution2d_59 (Convolution2D) (None, 8, 20, 28) 3080 merge_10[0][0]
____________________________________________________________________________________________________
leakyrelu_37 (LeakyReLU) (None, 48, 20, 28) 0 convolution2d_56[0][0]
____________________________________________________________________________________________________
leakyrelu_39 (LeakyReLU) (None, 8, 20, 28) 0 convolution2d_59[0][0]
____________________________________________________________________________________________________
convolution2d_57 (Convolution2D) (None, 64, 20, 28) 9280 leakyrelu_37[0][0]
____________________________________________________________________________________________________
convolution2d_60 (Convolution2D) (None, 16, 20, 28) 656 leakyrelu_39[0][0]
____________________________________________________________________________________________________
batchnormalization_25 (BatchNorma(None, 64, 20, 28) 128 convolution2d_57[0][0]
____________________________________________________________________________________________________
batchnormalization_26 (BatchNorma(None, 16, 20, 28) 32 convolution2d_60[0][0]
____________________________________________________________________________________________________
leakyrelu_38 (LeakyReLU) (None, 64, 20, 28) 0 batchnormalization_25[0][0]
____________________________________________________________________________________________________
leakyrelu_40 (LeakyReLU) (None, 16, 20, 28) 0 batchnormalization_26[0][0]
____________________________________________________________________________________________________
maxpooling2d_7 (MaxPooling2D) (None, 384, 20, 28) 0 merge_10[0][0]
____________________________________________________________________________________________________
convolution2d_55 (Convolution2D) (None, 32, 20, 28) 12320 merge_10[0][0]
____________________________________________________________________________________________________
convolution2d_58 (Convolution2D) (None, 64, 20, 28) 12352 leakyrelu_38[0][0]
____________________________________________________________________________________________________
convolution2d_61 (Convolution2D) (None, 16, 20, 28) 1296 leakyrelu_40[0][0]
____________________________________________________________________________________________________
convolution2d_62 (Convolution2D) (None, 16, 20, 28) 6160 maxpooling2d_7[0][0]
____________________________________________________________________________________________________
merge_11 (Merge) (None, 128, 20, 28) 0 convolution2d_55[0][0]
convolution2d_58[0][0]
convolution2d_61[0][0]
convolution2d_62[0][0]
____________________________________________________________________________________________________
convolution2d_63 (Convolution2D) (None, 64, 40, 56) 4160 leakyrelu_11[0][0]
____________________________________________________________________________________________________
batchnormalization_27 (BatchNorma(None, 128, 20, 28) 256 merge_11[0][0]
____________________________________________________________________________________________________
batchnormalization_28 (BatchNorma(None, 64, 40, 56) 128 convolution2d_63[0][0]
____________________________________________________________________________________________________
leakyrelu_41 (LeakyReLU) (None, 128, 20, 28) 0 batchnormalization_27[0][0]
____________________________________________________________________________________________________
lambda_3 (Lambda) (None, 64, 40, 56) 0 batchnormalization_28[0][0]
____________________________________________________________________________________________________
dropout_7 (Dropout) (None, 128, 20, 28) 0 leakyrelu_41[0][0]
____________________________________________________________________________________________________
merge_12 (Merge) (None, 64, 40, 56) 0 leakyrelu_11[0][0]
lambda_3[0][0]
____________________________________________________________________________________________________
upsampling2d_3 (UpSampling2D) (None, 128, 40, 56) 0 dropout_7[0][0]
____________________________________________________________________________________________________
leakyrelu_42 (LeakyReLU) (None, 64, 40, 56) 0 merge_12[0][0]
____________________________________________________________________________________________________
merge_13 (Merge) (None, 192, 40, 56) 0 upsampling2d_3[0][0]
leakyrelu_42[0][0]
____________________________________________________________________________________________________
convolution2d_65 (Convolution2D) (None, 24, 40, 56) 4632 merge_13[0][0]
____________________________________________________________________________________________________
convolution2d_68 (Convolution2D) (None, 4, 40, 56) 772 merge_13[0][0]
____________________________________________________________________________________________________
leakyrelu_43 (LeakyReLU) (None, 24, 40, 56) 0 convolution2d_65[0][0]
____________________________________________________________________________________________________
leakyrelu_45 (LeakyReLU) (None, 4, 40, 56) 0 convolution2d_68[0][0]
____________________________________________________________________________________________________
convolution2d_66 (Convolution2D) (None, 32, 40, 56) 2336 leakyrelu_43[0][0]
____________________________________________________________________________________________________
convolution2d_69 (Convolution2D) (None, 8, 40, 56) 168 leakyrelu_45[0][0]
____________________________________________________________________________________________________
batchnormalization_29 (BatchNorma(None, 32, 40, 56) 64 convolution2d_66[0][0]
____________________________________________________________________________________________________
batchnormalization_30 (BatchNorma(None, 8, 40, 56) 16 convolution2d_69[0][0]
____________________________________________________________________________________________________
leakyrelu_44 (LeakyReLU) (None, 32, 40, 56) 0 batchnormalization_29[0][0]
____________________________________________________________________________________________________
leakyrelu_46 (LeakyReLU) (None, 8, 40, 56) 0 batchnormalization_30[0][0]
____________________________________________________________________________________________________
maxpooling2d_8 (MaxPooling2D) (None, 192, 40, 56) 0 merge_13[0][0]
____________________________________________________________________________________________________
convolution2d_64 (Convolution2D) (None, 16, 40, 56) 3088 merge_13[0][0]
____________________________________________________________________________________________________
convolution2d_67 (Convolution2D) (None, 32, 40, 56) 3104 leakyrelu_44[0][0]
____________________________________________________________________________________________________
convolution2d_70 (Convolution2D) (None, 8, 40, 56) 328 leakyrelu_46[0][0]
____________________________________________________________________________________________________
convolution2d_71 (Convolution2D) (None, 8, 40, 56) 1544 maxpooling2d_8[0][0]
____________________________________________________________________________________________________
merge_14 (Merge) (None, 64, 40, 56) 0 convolution2d_64[0][0]
convolution2d_67[0][0]
convolution2d_70[0][0]
convolution2d_71[0][0]
____________________________________________________________________________________________________
convolution2d_72 (Convolution2D) (None, 32, 80, 112) 1056 leakyrelu_5[0][0]
____________________________________________________________________________________________________
batchnormalization_31 (BatchNorma(None, 64, 40, 56) 128 merge_14[0][0]
____________________________________________________________________________________________________
batchnormalization_32 (BatchNorma(None, 32, 80, 112) 64 convolution2d_72[0][0]
____________________________________________________________________________________________________
leakyrelu_47 (LeakyReLU) (None, 64, 40, 56) 0 batchnormalization_31[0][0]
____________________________________________________________________________________________________
lambda_4 (Lambda) (None, 32, 80, 112) 0 batchnormalization_32[0][0]
____________________________________________________________________________________________________
dropout_8 (Dropout) (None, 64, 40, 56) 0 leakyrelu_47[0][0]
____________________________________________________________________________________________________
merge_15 (Merge) (None, 32, 80, 112) 0 leakyrelu_5[0][0]
lambda_4[0][0]
____________________________________________________________________________________________________
upsampling2d_4 (UpSampling2D) (None, 64, 80, 112) 0 dropout_8[0][0]
____________________________________________________________________________________________________
leakyrelu_48 (LeakyReLU) (None, 32, 80, 112) 0 merge_15[0][0]
____________________________________________________________________________________________________
merge_16 (Merge) (None, 96, 80, 112) 0 upsampling2d_4[0][0]
leakyrelu_48[0][0]
____________________________________________________________________________________________________
convolution2d_74 (Convolution2D) (None, 12, 80, 112) 1164 merge_16[0][0]
____________________________________________________________________________________________________
convolution2d_77 (Convolution2D) (None, 2, 80, 112) 194 merge_16[0][0]
____________________________________________________________________________________________________
leakyrelu_49 (LeakyReLU) (None, 12, 80, 112) 0 convolution2d_74[0][0]
____________________________________________________________________________________________________
leakyrelu_51 (LeakyReLU) (None, 2, 80, 112) 0 convolution2d_77[0][0]
____________________________________________________________________________________________________
convolution2d_75 (Convolution2D) (None, 16, 80, 112) 592 leakyrelu_49[0][0]
____________________________________________________________________________________________________
convolution2d_78 (Convolution2D) (None, 4, 80, 112) 44 leakyrelu_51[0][0]
____________________________________________________________________________________________________
batchnormalization_33 (BatchNorma(None, 16, 80, 112) 32 convolution2d_75[0][0]
____________________________________________________________________________________________________
batchnormalization_34 (BatchNorma(None, 4, 80, 112) 8 convolution2d_78[0][0]
____________________________________________________________________________________________________
leakyrelu_50 (LeakyReLU) (None, 16, 80, 112) 0 batchnormalization_33[0][0]
____________________________________________________________________________________________________
leakyrelu_52 (LeakyReLU) (None, 4, 80, 112) 0 batchnormalization_34[0][0]
____________________________________________________________________________________________________
maxpooling2d_9 (MaxPooling2D) (None, 96, 80, 112) 0 merge_16[0][0]
____________________________________________________________________________________________________
convolution2d_73 (Convolution2D) (None, 8, 80, 112) 776 merge_16[0][0]
____________________________________________________________________________________________________
convolution2d_76 (Convolution2D) (None, 16, 80, 112) 784 leakyrelu_50[0][0]
____________________________________________________________________________________________________
convolution2d_79 (Convolution2D) (None, 4, 80, 112) 84 leakyrelu_52[0][0]
____________________________________________________________________________________________________
convolution2d_80 (Convolution2D) (None, 4, 80, 112) 388 maxpooling2d_9[0][0]
____________________________________________________________________________________________________
merge_17 (Merge) (None, 32, 80, 112) 0 convolution2d_73[0][0]
convolution2d_76[0][0]
convolution2d_79[0][0]
convolution2d_80[0][0]
____________________________________________________________________________________________________
batchnormalization_35 (BatchNorma(None, 32, 80, 112) 64 merge_17[0][0]
____________________________________________________________________________________________________
leakyrelu_53 (LeakyReLU) (None, 32, 80, 112) 0 batchnormalization_35[0][0]
____________________________________________________________________________________________________
dropout_9 (Dropout) (None, 32, 80, 112) 0 leakyrelu_53[0][0]
____________________________________________________________________________________________________
convolution2d_81 (Convolution2D) (None, 1, 80, 112) 33 dropout_9[0][0]
____________________________________________________________________________________________________
main_output (Reshape) (None, 80, 112) 0 convolution2d_81[0][0]
====================================================================================================
Total params: 1858475
____________________________________________________________________________________________________
In [23]:
# load pre-trained model?
# model.load_weights('models/unet_inception_inv2_20160927-05-11-02_acc-0.70_weights.hdf5')In [ ]:
model_checkpoint = ModelCheckpoint('models/%s_weights.hdf5'%model_name, monitor='val_acc', save_best_only=True, save_weights_only=True)
early_stopping = keras.callbacks.EarlyStopping(patience=2, monitor='val_acc')
history3 = model.fit_generator(train_gen,
samples_per_epoch=400,
nb_epoch=350,
verbose=1,
validation_data=test_gen,
nb_val_samples=170,
callbacks=[
model_checkpoint,
# early_stopping
])In [132]:
%%time
score = model.evaluate_generator(test_gen, val_samples=150)
score=dict(zip(model.metrics_names,score))
print(score){'loss': 0.10378610715270042, 'mean_squared_error': 0.006491463553781311, 'acc': 0.69975000619888306}
CPU times: user 3.86 s, sys: 916 ms, total: 4.78 s
Wall time: 3.18 s
In [133]:
import arrow
ts=arrow.utcnow().format('YYYYMMDD-HH-mm-ss')
fn='models/{}_{}_acc-{:2.2f}.hdf5'.format(model_name,ts,score['acc'])
model.save(fn)
fnOut [133]:
'models/unet_inception_inv2_20160927-05-11-02_acc-0.70.hdf5'
In [134]:
wfn='models/{}_{}_acc-{:2.2f}_weights.hdf5'.format(model_name,ts,score['acc'])
model.save_weights(wfn)
wfnOut [134]:
'models/unet_inception_inv2_20160927-05-11-02_acc-0.70_weights.hdf5'
In [95]:
def plot_hist(history):
"""plot keras history object"""
for label in history.history:
if not label.startswith('val'):
plt.title(label)
plt.plot(history.history[label], label=label)
if 'val_' + label in history.history:
plt.plot(history.history['val_' + label], label=label)
plt.xlabel('epoch')
plt.show()
plot_hist(model.history)
In [46]:
# remove dropout
model = unet_inception_model(optimiser,img_cols,img_rows,LeakyReLU,dropout=0.0)
# load best checkpoint
model.load_weights('models/%s_weights.hdf5'%model_name)
# or load pre-trained model
# model.load_weights('models/unet_inception_inv2_20160927-05-11-02_acc-0.70_weights.hdf5')In [47]:
score = model.evaluate_generator(test_gen, val_samples=batch_size)
score=dict(zip(model.metrics_names,score))
print(score){'acc': 0.70276403033694024, 'loss': 0.10917609349729204}
In [48]:
score = model.evaluate_generator(train_gen_unaugumented, val_samples=batch_size)
score=dict(zip(model.metrics_names,score))
print(score){'acc': 0.71395832896232603, 'loss': 0.074744646375377977}
In [61]:
X_test, y_test = next(test_gen)
y_pred = model.predict(X_test)In [62]:
from matplotlib.colors import LogNorm
norm=LogNorm(vmin=1e-3, vmax=1.0)
sns.set_style("dark")
n=10
plt.figure(figsize=(9,2.5*n))
fontsize=16
for i in range(n):
ax=plt.subplot(n,3,1+i*3)
if i==0: plt.title('(a) Input image', fontsize=fontsize)
plt.imshow(np.transpose(X_test,(0,2,3,1))[i])
plt.axis('off')
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
ax=plt.subplot(n,3,2+i*3)
if i==0: plt.title('(a) Manual mask', fontsize=fontsize)
plt.imshow(np.transpose(X_test,(0,2,3,1))[i])
cm=plt.imshow(y_test[i]>0.5, norm=norm, alpha=1, cmap=plt.cm.rainbow)
plt.axis('off')
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
ax=plt.subplot(n,3,3+i*3)
if i==0: plt.title('(b) Predicted mask', fontsize=fontsize)
plt.imshow(np.transpose(X_test,(0,2,3,1))[i])
cm=plt.imshow(y_pred[i]>0.5, norm=norm, alpha=1, cmap=plt.cm.rainbow)
plt.axis('off')
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
plt.tight_layout()
plt.savefig('images/results.png')
plt.show()
In [ ]: