Phân loại hoa sử dụng pretrain model
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Ở bài viết này, chúng ta sẽ sử dụng pretrain model của kares để phân loại hoa hồng (rose), hoa mặt trời (sunflower), hoa bồ công anh (dandelion), hoa cúc (daisy) và hoa tulip.
Lời mở đầu
Ở trong bài viết này, chúng ta sẽ sử dụng tập dữ liệu là tập dữ liệu ở ở link https://www.kaggle.com/alxmamaev/flowers-recognition. Tập dữ liệu này bao gồm 4242 hình cảnh của 5 loại hoa hồng (rose), hoa mặt trời (sunflower), hoa bồ công anh (dandelion), hoa cúc (daisy) và hoa tulip. Nhóm tác giả đã thu thập dữ liệu dựa trên các trang web flicr, google images, yandex. Tập hình ảnh được chia thành 5 lớp, mỗi lớp có khoảng 800 hình, có kích thước xấp xỉ 320x320 pixel. Các hình ảnh có kích thước không đồng nhất với nhau.
Thực hiện
Dữ liệu sau khi giản nén có dạng
data_dir/classname1/*.*
data_dir/classname2/*.*
...
Cấu trúc lưu trũ như này đúng với mô hình của mình nên chúng ta cần nên chúng ta không thay đổi gì về câu trúc nữa, tiến hành viết code
Đầu tiên, chúng ta sẽ load dataset lên và tranform nó để đưa vào huấn luyện.
import sys
import os
from collections import defaultdict
import numpy as np
import scipy.misc
def preprocess_input(x0):
x = x0 / 255.
x -= 0.5
x *= 2.
return x
def reverse_preprocess_input(x0):
x = x0 / 2.0
x += 0.5
x *= 255.
return x
def dataset(base_dir, n):
print("base dir: "+base_dir)
print("n: "+str(n))
n = int(n)
d = defaultdict(list)
for root, subdirs, files in os.walk(base_dir):
for filename in files:
file_path = os.path.join(root, filename)
assert file_path.startswith(base_dir)
suffix = file_path[len(base_dir):]
suffix = suffix.lstrip("/")
suffix = suffix.lstrip("\\")
if(suffix.find('/')>-1): #linux
label = suffix.split("/")[0]
else: #window
label = suffix.split("\\")[0]
d[label].append(file_path)
print("walk directory complete")
tags = sorted(d.keys())
processed_image_count = 0
useful_image_count = 0
X = []
y = []
for class_index, class_name in enumerate(tags):
filenames = d[class_name]
for filename in filenames:
processed_image_count += 1
if processed_image_count%100 ==0:
print(class_name+"\tprocess: "+str(processed_image_count)+"\t"+str(len(d[class_name])))
img = scipy.misc.imread(filename)
height, width, chan = img.shape
assert chan == 3
aspect_ratio = float(max((height, width))) / min((height, width))
if aspect_ratio > 2:
continue
# We pick the largest center square.
centery = height // 2
centerx = width // 2
radius = min((centerx, centery))
img = img[centery-radius:centery+radius, centerx-radius:centerx+radius]
img = scipy.misc.imresize(img, size=(n, n), interp='bilinear')
X.append(img)
y.append(class_index)
useful_image_count += 1
print("processed %d, used %d" % (processed_image_count, useful_image_count))
X = np.array(X).astype(np.float32)
#X = X.transpose((0, 3, 1, 2))
X = preprocess_input(X)
y = np.array(y)
perm = np.random.permutation(len(y))
X = X[perm]
y = y[perm]
print("classes:",end=" ")
for class_index, class_name in enumerate(tags):
print(class_name, sum(y==class_index),end=" ")
print("X shape: ",X.shape)
return X, y, tags
Đoạn code trên khá đơn giản và dễ hiểu. Lưu ý ở đây là với những bức ảnh có tỷ lệ width và height > 2 thì mình sẽ loại chúng ra khỏi tập dữ liệu.
Tiếp theo, chúng ta sẽ xây dựng mô hình dựa trên mô hình Resnet50 có sẵn của kares, do sử dụng pretrain model, nên n-1 lớp trước đó sẽ không được huấn luyện và chúng ta sẽ sử dụng dụng các weight có sẵn đã được huấn luyện trên tập ImageNet rút đặc trưng cho mô hình. Chúng ta chỉ cần thêm một lớp full connected và softmax để phân lớp các loại hoa, công việc của chúng ta hiện tại là tìm ra trọng số của lớp full connected cuối cùng (thay vì huấn luyện lại hết toàn bộ mô hình).
# create the base pre-trained model
def build_model(nb_classes):
base_model = ResNet50(weights='imagenet', include_top=False)
# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(1024, activation='relu')(x)
# and a logistic layer
predictions = Dense(nb_classes, activation='softmax')(x)
# this is the model we will train
model = Model(inputs=base_model.input, outputs=predictions)
# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional ResNet50 layers
for layer in base_model.layers:
layer.trainable = False
return model
Visualize một chút xíu về kiến trúc inceptionV3 mình đang dùng.
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, None, None, 3 0
__________________________________________________________________________________________________
conv1_pad (ZeroPadding2D) (None, None, None, 3 0 input_1[0][0]
__________________________________________________________________________________________________
conv1 (Conv2D) (None, None, None, 6 9472 conv1_pad[0][0]
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization) (None, None, None, 6 256 conv1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, None, None, 6 0 bn_conv1[0][0]
__________________________________________________________________________________________________
pool1_pad (ZeroPadding2D) (None, None, None, 6 0 activation_1[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, None, None, 6 0 pool1_pad[0][0]
__________________________________________________________________________________________________
res2a_branch2a (Conv2D) (None, None, None, 6 4160 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
bn2a_branch2a (BatchNormalizati (None, None, None, 6 256 res2a_branch2a[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, None, None, 6 0 bn2a_branch2a[0][0]
__________________________________________________________________________________________________
res2a_branch2b (Conv2D) (None, None, None, 6 36928 activation_2[0][0]
__________________________________________________________________________________________________
bn2a_branch2b (BatchNormalizati (None, None, None, 6 256 res2a_branch2b[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, None, None, 6 0 bn2a_branch2b[0][0]
__________________________________________________________________________________________________
res2a_branch2c (Conv2D) (None, None, None, 2 16640 activation_3[0][0]
__________________________________________________________________________________________________
res2a_branch1 (Conv2D) (None, None, None, 2 16640 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
bn2a_branch2c (BatchNormalizati (None, None, None, 2 1024 res2a_branch2c[0][0]
__________________________________________________________________________________________________
bn2a_branch1 (BatchNormalizatio (None, None, None, 2 1024 res2a_branch1[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, None, None, 2 0 bn2a_branch2c[0][0]
bn2a_branch1[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, None, None, 2 0 add_1[0][0]
__________________________________________________________________________________________________
res2b_branch2a (Conv2D) (None, None, None, 6 16448 activation_4[0][0]
__________________________________________________________________________________________________
bn2b_branch2a (BatchNormalizati (None, None, None, 6 256 res2b_branch2a[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, None, None, 6 0 bn2b_branch2a[0][0]
__________________________________________________________________________________________________
res2b_branch2b (Conv2D) (None, None, None, 6 36928 activation_5[0][0]
__________________________________________________________________________________________________
bn2b_branch2b (BatchNormalizati (None, None, None, 6 256 res2b_branch2b[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, None, None, 6 0 bn2b_branch2b[0][0]
__________________________________________________________________________________________________
res2b_branch2c (Conv2D) (None, None, None, 2 16640 activation_6[0][0]
__________________________________________________________________________________________________
bn2b_branch2c (BatchNormalizati (None, None, None, 2 1024 res2b_branch2c[0][0]
__________________________________________________________________________________________________
add_2 (Add) (None, None, None, 2 0 bn2b_branch2c[0][0]
activation_4[0][0]
__________________________________________________________________________________________________
activation_7 (Activation) (None, None, None, 2 0 add_2[0][0]
__________________________________________________________________________________________________
res2c_branch2a (Conv2D) (None, None, None, 6 16448 activation_7[0][0]
__________________________________________________________________________________________________
bn2c_branch2a (BatchNormalizati (None, None, None, 6 256 res2c_branch2a[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, None, None, 6 0 bn2c_branch2a[0][0]
__________________________________________________________________________________________________
res2c_branch2b (Conv2D) (None, None, None, 6 36928 activation_8[0][0]
__________________________________________________________________________________________________
bn2c_branch2b (BatchNormalizati (None, None, None, 6 256 res2c_branch2b[0][0]
__________________________________________________________________________________________________
activation_9 (Activation) (None, None, None, 6 0 bn2c_branch2b[0][0]
__________________________________________________________________________________________________
res2c_branch2c (Conv2D) (None, None, None, 2 16640 activation_9[0][0]
__________________________________________________________________________________________________
bn2c_branch2c (BatchNormalizati (None, None, None, 2 1024 res2c_branch2c[0][0]
__________________________________________________________________________________________________
add_3 (Add) (None, None, None, 2 0 bn2c_branch2c[0][0]
activation_7[0][0]
__________________________________________________________________________________________________
activation_10 (Activation) (None, None, None, 2 0 add_3[0][0]
__________________________________________________________________________________________________
res3a_branch2a (Conv2D) (None, None, None, 1 32896 activation_10[0][0]
__________________________________________________________________________________________________
bn3a_branch2a (BatchNormalizati (None, None, None, 1 512 res3a_branch2a[0][0]
__________________________________________________________________________________________________
activation_11 (Activation) (None, None, None, 1 0 bn3a_branch2a[0][0]
__________________________________________________________________________________________________
res3a_branch2b (Conv2D) (None, None, None, 1 147584 activation_11[0][0]
__________________________________________________________________________________________________
bn3a_branch2b (BatchNormalizati (None, None, None, 1 512 res3a_branch2b[0][0]
__________________________________________________________________________________________________
activation_12 (Activation) (None, None, None, 1 0 bn3a_branch2b[0][0]
__________________________________________________________________________________________________
res3a_branch2c (Conv2D) (None, None, None, 5 66048 activation_12[0][0]
__________________________________________________________________________________________________
res3a_branch1 (Conv2D) (None, None, None, 5 131584 activation_10[0][0]
__________________________________________________________________________________________________
bn3a_branch2c (BatchNormalizati (None, None, None, 5 2048 res3a_branch2c[0][0]
__________________________________________________________________________________________________
bn3a_branch1 (BatchNormalizatio (None, None, None, 5 2048 res3a_branch1[0][0]
__________________________________________________________________________________________________
add_4 (Add) (None, None, None, 5 0 bn3a_branch2c[0][0]
bn3a_branch1[0][0]
__________________________________________________________________________________________________
activation_13 (Activation) (None, None, None, 5 0 add_4[0][0]
__________________________________________________________________________________________________
res3b_branch2a (Conv2D) (None, None, None, 1 65664 activation_13[0][0]
__________________________________________________________________________________________________
bn3b_branch2a (BatchNormalizati (None, None, None, 1 512 res3b_branch2a[0][0]
__________________________________________________________________________________________________
activation_14 (Activation) (None, None, None, 1 0 bn3b_branch2a[0][0]
__________________________________________________________________________________________________
res3b_branch2b (Conv2D) (None, None, None, 1 147584 activation_14[0][0]
__________________________________________________________________________________________________
bn3b_branch2b (BatchNormalizati (None, None, None, 1 512 res3b_branch2b[0][0]
__________________________________________________________________________________________________
activation_15 (Activation) (None, None, None, 1 0 bn3b_branch2b[0][0]
__________________________________________________________________________________________________
res3b_branch2c (Conv2D) (None, None, None, 5 66048 activation_15[0][0]
__________________________________________________________________________________________________
bn3b_branch2c (BatchNormalizati (None, None, None, 5 2048 res3b_branch2c[0][0]
__________________________________________________________________________________________________
add_5 (Add) (None, None, None, 5 0 bn3b_branch2c[0][0]
activation_13[0][0]
__________________________________________________________________________________________________
activation_16 (Activation) (None, None, None, 5 0 add_5[0][0]
__________________________________________________________________________________________________
res3c_branch2a (Conv2D) (None, None, None, 1 65664 activation_16[0][0]
__________________________________________________________________________________________________
bn3c_branch2a (BatchNormalizati (None, None, None, 1 512 res3c_branch2a[0][0]
__________________________________________________________________________________________________
activation_17 (Activation) (None, None, None, 1 0 bn3c_branch2a[0][0]
__________________________________________________________________________________________________
res3c_branch2b (Conv2D) (None, None, None, 1 147584 activation_17[0][0]
__________________________________________________________________________________________________
bn3c_branch2b (BatchNormalizati (None, None, None, 1 512 res3c_branch2b[0][0]
__________________________________________________________________________________________________
activation_18 (Activation) (None, None, None, 1 0 bn3c_branch2b[0][0]
__________________________________________________________________________________________________
res3c_branch2c (Conv2D) (None, None, None, 5 66048 activation_18[0][0]
__________________________________________________________________________________________________
bn3c_branch2c (BatchNormalizati (None, None, None, 5 2048 res3c_branch2c[0][0]
__________________________________________________________________________________________________
add_6 (Add) (None, None, None, 5 0 bn3c_branch2c[0][0]
activation_16[0][0]
__________________________________________________________________________________________________
activation_19 (Activation) (None, None, None, 5 0 add_6[0][0]
__________________________________________________________________________________________________
res3d_branch2a (Conv2D) (None, None, None, 1 65664 activation_19[0][0]
__________________________________________________________________________________________________
bn3d_branch2a (BatchNormalizati (None, None, None, 1 512 res3d_branch2a[0][0]
__________________________________________________________________________________________________
activation_20 (Activation) (None, None, None, 1 0 bn3d_branch2a[0][0]
__________________________________________________________________________________________________
res3d_branch2b (Conv2D) (None, None, None, 1 147584 activation_20[0][0]
__________________________________________________________________________________________________
bn3d_branch2b (BatchNormalizati (None, None, None, 1 512 res3d_branch2b[0][0]
__________________________________________________________________________________________________
activation_21 (Activation) (None, None, None, 1 0 bn3d_branch2b[0][0]
__________________________________________________________________________________________________
res3d_branch2c (Conv2D) (None, None, None, 5 66048 activation_21[0][0]
__________________________________________________________________________________________________
bn3d_branch2c (BatchNormalizati (None, None, None, 5 2048 res3d_branch2c[0][0]
__________________________________________________________________________________________________
add_7 (Add) (None, None, None, 5 0 bn3d_branch2c[0][0]
activation_19[0][0]
__________________________________________________________________________________________________
activation_22 (Activation) (None, None, None, 5 0 add_7[0][0]
__________________________________________________________________________________________________
res4a_branch2a (Conv2D) (None, None, None, 2 131328 activation_22[0][0]
__________________________________________________________________________________________________
bn4a_branch2a (BatchNormalizati (None, None, None, 2 1024 res4a_branch2a[0][0]
__________________________________________________________________________________________________
activation_23 (Activation) (None, None, None, 2 0 bn4a_branch2a[0][0]
__________________________________________________________________________________________________
res4a_branch2b (Conv2D) (None, None, None, 2 590080 activation_23[0][0]
__________________________________________________________________________________________________
bn4a_branch2b (BatchNormalizati (None, None, None, 2 1024 res4a_branch2b[0][0]
__________________________________________________________________________________________________
activation_24 (Activation) (None, None, None, 2 0 bn4a_branch2b[0][0]
__________________________________________________________________________________________________
res4a_branch2c (Conv2D) (None, None, None, 1 263168 activation_24[0][0]
__________________________________________________________________________________________________
res4a_branch1 (Conv2D) (None, None, None, 1 525312 activation_22[0][0]
__________________________________________________________________________________________________
bn4a_branch2c (BatchNormalizati (None, None, None, 1 4096 res4a_branch2c[0][0]
__________________________________________________________________________________________________
bn4a_branch1 (BatchNormalizatio (None, None, None, 1 4096 res4a_branch1[0][0]
__________________________________________________________________________________________________
add_8 (Add) (None, None, None, 1 0 bn4a_branch2c[0][0]
bn4a_branch1[0][0]
__________________________________________________________________________________________________
activation_25 (Activation) (None, None, None, 1 0 add_8[0][0]
__________________________________________________________________________________________________
res4b_branch2a (Conv2D) (None, None, None, 2 262400 activation_25[0][0]
__________________________________________________________________________________________________
bn4b_branch2a (BatchNormalizati (None, None, None, 2 1024 res4b_branch2a[0][0]
__________________________________________________________________________________________________
activation_26 (Activation) (None, None, None, 2 0 bn4b_branch2a[0][0]
__________________________________________________________________________________________________
res4b_branch2b (Conv2D) (None, None, None, 2 590080 activation_26[0][0]
__________________________________________________________________________________________________
bn4b_branch2b (BatchNormalizati (None, None, None, 2 1024 res4b_branch2b[0][0]
__________________________________________________________________________________________________
activation_27 (Activation) (None, None, None, 2 0 bn4b_branch2b[0][0]
__________________________________________________________________________________________________
res4b_branch2c (Conv2D) (None, None, None, 1 263168 activation_27[0][0]
__________________________________________________________________________________________________
bn4b_branch2c (BatchNormalizati (None, None, None, 1 4096 res4b_branch2c[0][0]
__________________________________________________________________________________________________
add_9 (Add) (None, None, None, 1 0 bn4b_branch2c[0][0]
activation_25[0][0]
__________________________________________________________________________________________________
activation_28 (Activation) (None, None, None, 1 0 add_9[0][0]
__________________________________________________________________________________________________
res4c_branch2a (Conv2D) (None, None, None, 2 262400 activation_28[0][0]
__________________________________________________________________________________________________
bn4c_branch2a (BatchNormalizati (None, None, None, 2 1024 res4c_branch2a[0][0]
__________________________________________________________________________________________________
activation_29 (Activation) (None, None, None, 2 0 bn4c_branch2a[0][0]
__________________________________________________________________________________________________
res4c_branch2b (Conv2D) (None, None, None, 2 590080 activation_29[0][0]
__________________________________________________________________________________________________
bn4c_branch2b (BatchNormalizati (None, None, None, 2 1024 res4c_branch2b[0][0]
__________________________________________________________________________________________________
activation_30 (Activation) (None, None, None, 2 0 bn4c_branch2b[0][0]
__________________________________________________________________________________________________
res4c_branch2c (Conv2D) (None, None, None, 1 263168 activation_30[0][0]
__________________________________________________________________________________________________
bn4c_branch2c (BatchNormalizati (None, None, None, 1 4096 res4c_branch2c[0][0]
__________________________________________________________________________________________________
add_10 (Add) (None, None, None, 1 0 bn4c_branch2c[0][0]
activation_28[0][0]
__________________________________________________________________________________________________
activation_31 (Activation) (None, None, None, 1 0 add_10[0][0]
__________________________________________________________________________________________________
res4d_branch2a (Conv2D) (None, None, None, 2 262400 activation_31[0][0]
__________________________________________________________________________________________________
bn4d_branch2a (BatchNormalizati (None, None, None, 2 1024 res4d_branch2a[0][0]
__________________________________________________________________________________________________
activation_32 (Activation) (None, None, None, 2 0 bn4d_branch2a[0][0]
__________________________________________________________________________________________________
res4d_branch2b (Conv2D) (None, None, None, 2 590080 activation_32[0][0]
__________________________________________________________________________________________________
bn4d_branch2b (BatchNormalizati (None, None, None, 2 1024 res4d_branch2b[0][0]
__________________________________________________________________________________________________
activation_33 (Activation) (None, None, None, 2 0 bn4d_branch2b[0][0]
__________________________________________________________________________________________________
res4d_branch2c (Conv2D) (None, None, None, 1 263168 activation_33[0][0]
__________________________________________________________________________________________________
bn4d_branch2c (BatchNormalizati (None, None, None, 1 4096 res4d_branch2c[0][0]
__________________________________________________________________________________________________
add_11 (Add) (None, None, None, 1 0 bn4d_branch2c[0][0]
activation_31[0][0]
__________________________________________________________________________________________________
activation_34 (Activation) (None, None, None, 1 0 add_11[0][0]
__________________________________________________________________________________________________
res4e_branch2a (Conv2D) (None, None, None, 2 262400 activation_34[0][0]
__________________________________________________________________________________________________
bn4e_branch2a (BatchNormalizati (None, None, None, 2 1024 res4e_branch2a[0][0]
__________________________________________________________________________________________________
activation_35 (Activation) (None, None, None, 2 0 bn4e_branch2a[0][0]
__________________________________________________________________________________________________
res4e_branch2b (Conv2D) (None, None, None, 2 590080 activation_35[0][0]
__________________________________________________________________________________________________
bn4e_branch2b (BatchNormalizati (None, None, None, 2 1024 res4e_branch2b[0][0]
__________________________________________________________________________________________________
activation_36 (Activation) (None, None, None, 2 0 bn4e_branch2b[0][0]
__________________________________________________________________________________________________
res4e_branch2c (Conv2D) (None, None, None, 1 263168 activation_36[0][0]
__________________________________________________________________________________________________
bn4e_branch2c (BatchNormalizati (None, None, None, 1 4096 res4e_branch2c[0][0]
__________________________________________________________________________________________________
add_12 (Add) (None, None, None, 1 0 bn4e_branch2c[0][0]
activation_34[0][0]
__________________________________________________________________________________________________
activation_37 (Activation) (None, None, None, 1 0 add_12[0][0]
__________________________________________________________________________________________________
res4f_branch2a (Conv2D) (None, None, None, 2 262400 activation_37[0][0]
__________________________________________________________________________________________________
bn4f_branch2a (BatchNormalizati (None, None, None, 2 1024 res4f_branch2a[0][0]
__________________________________________________________________________________________________
activation_38 (Activation) (None, None, None, 2 0 bn4f_branch2a[0][0]
__________________________________________________________________________________________________
res4f_branch2b (Conv2D) (None, None, None, 2 590080 activation_38[0][0]
__________________________________________________________________________________________________
bn4f_branch2b (BatchNormalizati (None, None, None, 2 1024 res4f_branch2b[0][0]
__________________________________________________________________________________________________
activation_39 (Activation) (None, None, None, 2 0 bn4f_branch2b[0][0]
__________________________________________________________________________________________________
res4f_branch2c (Conv2D) (None, None, None, 1 263168 activation_39[0][0]
__________________________________________________________________________________________________
bn4f_branch2c (BatchNormalizati (None, None, None, 1 4096 res4f_branch2c[0][0]
__________________________________________________________________________________________________
add_13 (Add) (None, None, None, 1 0 bn4f_branch2c[0][0]
activation_37[0][0]
__________________________________________________________________________________________________
activation_40 (Activation) (None, None, None, 1 0 add_13[0][0]
__________________________________________________________________________________________________
res5a_branch2a (Conv2D) (None, None, None, 5 524800 activation_40[0][0]
__________________________________________________________________________________________________
bn5a_branch2a (BatchNormalizati (None, None, None, 5 2048 res5a_branch2a[0][0]
__________________________________________________________________________________________________
activation_41 (Activation) (None, None, None, 5 0 bn5a_branch2a[0][0]
__________________________________________________________________________________________________
res5a_branch2b (Conv2D) (None, None, None, 5 2359808 activation_41[0][0]
__________________________________________________________________________________________________
bn5a_branch2b (BatchNormalizati (None, None, None, 5 2048 res5a_branch2b[0][0]
__________________________________________________________________________________________________
activation_42 (Activation) (None, None, None, 5 0 bn5a_branch2b[0][0]
__________________________________________________________________________________________________
res5a_branch2c (Conv2D) (None, None, None, 2 1050624 activation_42[0][0]
__________________________________________________________________________________________________
res5a_branch1 (Conv2D) (None, None, None, 2 2099200 activation_40[0][0]
__________________________________________________________________________________________________
bn5a_branch2c (BatchNormalizati (None, None, None, 2 8192 res5a_branch2c[0][0]
__________________________________________________________________________________________________
bn5a_branch1 (BatchNormalizatio (None, None, None, 2 8192 res5a_branch1[0][0]
__________________________________________________________________________________________________
add_14 (Add) (None, None, None, 2 0 bn5a_branch2c[0][0]
bn5a_branch1[0][0]
__________________________________________________________________________________________________
activation_43 (Activation) (None, None, None, 2 0 add_14[0][0]
__________________________________________________________________________________________________
res5b_branch2a (Conv2D) (None, None, None, 5 1049088 activation_43[0][0]
__________________________________________________________________________________________________
bn5b_branch2a (BatchNormalizati (None, None, None, 5 2048 res5b_branch2a[0][0]
__________________________________________________________________________________________________
activation_44 (Activation) (None, None, None, 5 0 bn5b_branch2a[0][0]
__________________________________________________________________________________________________
res5b_branch2b (Conv2D) (None, None, None, 5 2359808 activation_44[0][0]
__________________________________________________________________________________________________
bn5b_branch2b (BatchNormalizati (None, None, None, 5 2048 res5b_branch2b[0][0]
__________________________________________________________________________________________________
activation_45 (Activation) (None, None, None, 5 0 bn5b_branch2b[0][0]
__________________________________________________________________________________________________
res5b_branch2c (Conv2D) (None, None, None, 2 1050624 activation_45[0][0]
__________________________________________________________________________________________________
bn5b_branch2c (BatchNormalizati (None, None, None, 2 8192 res5b_branch2c[0][0]
__________________________________________________________________________________________________
add_15 (Add) (None, None, None, 2 0 bn5b_branch2c[0][0]
activation_43[0][0]
__________________________________________________________________________________________________
activation_46 (Activation) (None, None, None, 2 0 add_15[0][0]
__________________________________________________________________________________________________
res5c_branch2a (Conv2D) (None, None, None, 5 1049088 activation_46[0][0]
__________________________________________________________________________________________________
bn5c_branch2a (BatchNormalizati (None, None, None, 5 2048 res5c_branch2a[0][0]
__________________________________________________________________________________________________
activation_47 (Activation) (None, None, None, 5 0 bn5c_branch2a[0][0]
__________________________________________________________________________________________________
res5c_branch2b (Conv2D) (None, None, None, 5 2359808 activation_47[0][0]
__________________________________________________________________________________________________
bn5c_branch2b (BatchNormalizati (None, None, None, 5 2048 res5c_branch2b[0][0]
__________________________________________________________________________________________________
activation_48 (Activation) (None, None, None, 5 0 bn5c_branch2b[0][0]
__________________________________________________________________________________________________
res5c_branch2c (Conv2D) (None, None, None, 2 1050624 activation_48[0][0]
__________________________________________________________________________________________________
bn5c_branch2c (BatchNormalizati (None, None, None, 2 8192 res5c_branch2c[0][0]
__________________________________________________________________________________________________
add_16 (Add) (None, None, None, 2 0 bn5c_branch2c[0][0]
activation_46[0][0]
__________________________________________________________________________________________________
activation_49 (Activation) (None, None, None, 2 0 add_16[0][0]
__________________________________________________________________________________________________
global_average_pooling2d_1 (Glo (None, 2048) 0 activation_49[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 1024) 2098176 global_average_pooling2d_1[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 5) 5125 dense_1[0][0]
==================================================================================================
Total params: 25,691,013
Trainable params: 2,103,301
Non-trainable params: 23,587,712
__________________________________________________________________________________________________
Phần train lại sẽ có khoảng hơn 2 triệu tham số, phần layer ở trước đó không train là khoảng 23 triệu tham số.
Chia tập dữ liệu ra thành 5 phần, 4 phần làm tập train, 1 phần làm tập validation.
X, y, tags = dataset.dataset(data_directory, n)
nb_classes = len(tags)
sample_count = len(y)
train_size = sample_count * 4 // 5
X_train = X[:train_size]
y_train = y[:train_size]
Y_train = np_utils.to_categorical(y_train, nb_classes)
X_test = X[train_size:]
y_test = y[train_size:]
Y_test = np_utils.to_categorical(y_test, nb_classes)
chúng ta tiến hành thực hiện ImageDataGenerator để có được nhiều dữ liệu mẫu hơn và chống overfit, trong keras đã có sẵn hàm
datagen = ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=45,
width_shift_range=0.25,
height_shift_range=0.25,
horizontal_flip=True,
vertical_flip=False,
channel_shift_range=0.5,
zoom_range=[0.5, 1.5],
brightness_range=[0.5, 1.5],
fill_mode='reflect')
datagen.fit(X_train)
Cuối cùng, chúng ta sẽ xây dựng mô hình và tiến hành huấn luyện, lưu mô hình. Quá trình này tốn hơi nhiều thời gian.
model = net.build_model(nb_classes)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=["accuracy"])
# train the model on the new data for a few epochs
print("training the newly added dense layers")
samples_per_epoch = X_train.shape[0]//batch_size*batch_size
steps_per_epoch = samples_per_epoch//batch_size
validation_steps = X_test.shape[0]//batch_size*batch_size
model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size, shuffle=True),
samples_per_epoch=samples_per_epoch,
epochs=nb_epoch,
steps_per_epoch = steps_per_epoch,
validation_data=datagen.flow(X_test, Y_test, batch_size=batch_size),
validation_steps=validation_steps,
)
net.save(model, tags, model_file_prefix)
Thử download một vài hình ảnh trên mạng về rồi test thử xem sao
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