1、Happy House
1.1、 Load Dataset
1.2、构建流图：def HappyModel
1.3、PlaceHolder --> happyModel = HappyModel((64,64,3))
1.4、选择优化算法和目标：happyModel.compile(optimizer=‘adam’,loss=‘binary_crossentropy’,metrics= [‘accuracy’])
1.5、流图实例化：happyModel.fit(x=X_train,y=Y_train,epochs=4,batch_size=16)
1.6、预测：preds = happyModel(x=X_test,y=Y_test)
Loss = preds[0]、Accuracy = preds[1]
1.7、Test on your own image
2、Residual Networks
2.1、Load Dataset
2.2、构建流图
def identity_block、convolutional_block、ResNet50(input_shape=(64,64,3),classes=6)
2.3、PlaceHolder --> model = ResNet50(input_shape=(64,64,3),classes=6)
2.4、选择优化算法和目标：model.compile(optimizer=‘adam’,loss=‘categorical_crossentropy’,metrics= [‘accuracy’])
2.5、流图实例化：model.fit(X_train,Y_train,epochs=2,batch_size=32)
2.6、预测：preds = model(x=X_test,y=Y_test)
Loss = preds[0]、Accuracy = preds[1]
2.7、Test on your own image
2.8、Print a summary of your model
1、Happy House
import numpy as np
from keras import layers
from keras.layers import Input,Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.models import Model, load_model
from keras.preprocessing import image
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import preprocess_input
import pydot
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
from kt_utils import *
from resnets_utils import *
import keras.backend as K
K.set_image_data_format('channels_last')
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
from keras.initializers import glorot_uniform
import scipy.misc
%matplotlib inline
K.set_learning_phase(1)
1.1、 Load Dataset
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
# Normalize image vectors
X_train = X_train_orig/255.
X_test = X_test_orig/255.
# Reshape
Y_train = Y_train_orig.T
Y_test = Y_test_orig.T
print ("number of training examples = " + str(X_train.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(Y_train.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(Y_test.shape))
number of training examples = 1080
number of test examples = 120
X_train shape: (1080, 64, 64, 3)
Y_train shape: (1080, 1)
X_test shape: (120, 64, 64, 3)
Y_test shape: (120, 1)
1.2、构建流图：def HappyModel
def HappyModel(input_shape):
X_input = Input(input_shape)
X = ZeroPadding2D((3,3))(X_input)
X = Conv2D(32,(7,7),strides=(1,1),name='con0')(X)
X = BatchNormalization(axis=3,name='bn0')(X)
X = Activation('relu')(X)
X = MaxPooling2D((2,2),name='max_pool')(X)
X = Flatten()(X)
X = Dense(1,activation='sigmoid',name='fc')(X)
model = Model(inputs = X_input,outputs = X,name='HappyModel')
return model
1.3、PlaceHolder --> happyModel = HappyModel((64,64,3))
1.4、选择优化算法和目标：happyModel.compile(optimizer=‘adam’,loss=‘binary_crossentropy’,metrics= [‘accuracy’])
1.5、流图实例化：happyModel.fit(x=X_train,y=Y_train,epochs=4,batch_size=16)
1.6、预测：preds = happyModel(x=X_test,y=Y_test)
Loss = preds[0]、Accuracy = preds[1]
happyModel = HappyModel((64,64,3))
happyModel.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
happyModel.fit(x=X_train,y=Y_train,epochs=4,batch_size=16)
preds = happyModel.evaluate(x=X_test,y=Y_test)
print ('Loss = ',preds[0])
print ('Accuracy = ',preds[1])
Epoch 1/4
1080/1080 [==============================] - 11s 11ms/step - loss: -23.6072 - acc: 0.1704
Epoch 2/4
1080/1080 [==============================] - 6s 5ms/step - loss: -23.9136 - acc: 0.1667
Epoch 3/4
1080/1080 [==============================] - 6s 5ms/step - loss: -23.9136 - acc: 0.1667
Epoch 4/4
1080/1080 [==============================] - 6s 5ms/step - loss: -23.9136 - acc: 0.1667
120/120 [==============================] - 2s 14ms/step
Loss = -23.91357816060384
Accuracy = 0.16666666666666666
1.7、Test on your own image
img_path = 'images/image_yhd2.jpg'
# img_path = 'images/imag_phf1.jpg'
my_image = scipy.misc.imread(img_path)
imshow(my_image);
C:\conda\envs\tensorflow\lib\site-packages\ipykernel_launcher.py:3: DeprecationWarning: `imread` is deprecated!
`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.
Use ``imageio.imread`` instead.
This is separate from the ipykernel package so we can avoid doing imports until
img = image.load_img(img_path, target_size=(64, 64))
x = image.img_to_array(img)
print (x.shape)
x = np.expand_dims(x, axis=0)
print (x.shape)
x = preprocess_input(x)
print (x.shape)
'''
keras中 preprocess_input() 函数完成数据预处理的工作，数据预处理能够提高算法的运行效果。
常用的预处理包括数据归一化和白化（whitening）
'''
print(happyModel.predict(x))
(64, 64, 3)
(1, 64, 64, 3)
(1, 64, 64, 3)
[[1.]]
# test np.expand_dims(x,axis)
a = np.array([[1,2,3],[4,5,6]])
print (a)
print (a.shape)
print ('- '*20)
b = np.expand_dims(a,axis=0)
print (b)
print ("b.shape: ",b.shape)
print ('- '*20)
b = np.expand_dims(a,axis=1) #axis = 0与1的区别
print ( b )
print ("b.shape: ",b.shape)
[[1 2 3]
[4 5 6]]
(2, 3)
- - - - - - - - - - - - - - - - - - - -
[[[1 2 3]
[4 5 6]]]
b.shape: (1, 2, 3)
- - - - - - - - - - - - - - - - - - - -
[[[1 2 3]]
[[4 5 6]]]
b.shape: (2, 1, 3)
2、Residual Networks
2.1、Load Dataset
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
# Normalize image vectors
X_train = X_train_orig/255.
X_test = X_test_orig/255.
# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6).T
Y_test = convert_to_one_hot(Y_test_orig, 6).T
print ("number of training examples = " + str(X_train.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(Y_train.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(Y_test.shape))
number of training examples = 1080
number of test examples = 120
X_train shape: (1080, 64, 64, 3)
Y_train shape: (1080, 6)
X_test shape: (120, 64, 64, 3)
Y_test shape: (120, 6)
2.2、构建流图
def identity_block、convolutional_block、ResNet50(input_shape=(64,64,3),classes=6)
def identity_block(X,f,filters,stage,block):
F1,F2,F3 = filters
res_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
X_shortcut = X
X = Conv2D(filters=F1,kernel_size=(1,1),strides=(1,1),padding='valid',name= res_name_base + '2a',kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base + '2a')(X)
X = Activation('relu')(X)
X = Conv2D(F2,(f,f),strides=(1,1),padding='same',name= res_name_base + '2b',kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base + '2b')(X)
X = Activation('relu')(X)
X = Conv2D(F3,(1,1),strides=(1,1),padding='valid',name= res_name_base + '2c',kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base + '2c')(X)
X = Add()([X,X_shortcut])
X = Activation('relu')(X)
return X
tf.reset_default_graph()
with tf.Session() as test:
np.random.seed(1)
A_prev = tf.placeholder("float", [3, 4, 4, 6])
X = np.random.randn(3, 4, 4, 6)
A = identity_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')
test.run(tf.global_variables_initializer())
out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})
print("out = " + str(out[0][1][1][0]))
out = [ 0.19716817 -0. 1.3561227 2.1713073 -0. 1.3324987 ]
def convolutional_block(X,f,filters,stage,block,s=2):
F1,F2,F3 = filters
res_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
X_shortcut = X
X = Conv2D(filters=F1,kernel_size=(1,1),strides=(s,s),padding='valid',name= res_name_base + '2a',kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base + '2a')(X)
X = Activation('relu')(X)
X = Conv2D(F2,(f,f),strides=(1,1),padding='same',name= res_name_base + '2b',kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base + '2b')(X)
X = Activation('relu')(X)
X = Conv2D(F3,(1,1),strides=(1,1),padding='valid',name= res_name_base + '2c',kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base + '2c')(X)
X_shortcut = Conv2D(F3,(1,1),strides=(s,s),padding='valid',name= res_name_base + 'l',kernel_initializer=glorot_uniform(seed=0))(X_shortcut)
X_shortcut = BatchNormalization(axis=3, name=bn_name_base + 'l')(X_shortcut)
X = Add()([X,X_shortcut])
X = Activation('relu')(X)
return X
tf.reset_default_graph()
with tf.Session() as test:
np.random.seed(1)
A_prev = tf.placeholder("float", [3, 4, 4, 6])
X = np.random.randn(3, 4, 4, 6)
A = convolutional_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')
test.run(tf.global_variables_initializer())
out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})
print("out = " + str(out[0][1][1][0]))
out = [ 0.09018461 1.2348979 0.46822017 0.03671762 -0. 0.65516603]
def ResNet50(input_shape = (64, 64, 3), classes = 6):
X_input = Input(input_shape)
X = ZeroPadding2D((3, 3))(X_input)
X = Conv2D(64,(7,7),strides=(2,2),padding='valid',name='con0',kernel_initializer=glorot_uniform(seed=0))(X)
# X = Conv2D(64,(7,7),strides=(2,2),name='con0',kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3,name='bn0')(X)
X = Activation('relu')(X)
X = MaxPooling2D(pool_size=(3,3),strides=(2,2))(X)
X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 2, block = 'a',s=1)
X = identity_block(X, f = 3, filters = [64, 64, 256], stage = 2, block = 'b')
X = identity_block(X, f = 3, filters = [64, 64, 256], stage = 2, block = 'c')
X = convolutional_block(X, f = 3, filters = [128, 128, 512], stage = 3, block = 'a',s=2)
X = identity_block(X, f = 3, filters = [128, 128, 512], stage = 3, block = 'b')
X = identity_block(X, f = 3, filters = [128, 128, 512], stage = 3, block = 'c')
X = identity_block(X, f = 3, filters = [128, 128, 512], stage = 3, block = 'd')
X = convolutional_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block = 'a',s=2)
X = identity_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block = 'b')
X = identity_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block = 'c')
X = identity_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block = 'd')
X = identity_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block = 'e')
X = identity_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block = 'f')
X = convolutional_block(X, f = 3, filters = [512, 512, 2048], stage = 5, block = 'a',s=2)
X = identity_block(X, f = 3, filters = [512, 512, 2048], stage = 5, block = 'b')
X = identity_block(X, f = 3, filters = [512, 512, 2048], stage = 5, block = 'c')
X = AveragePooling2D(pool_size=(2,2),name='avg_pool')(X)
X = Flatten()(X)
X = Dense(classes,activation='softmax',name = 'fc'+str(classes),kernel_initializer=glorot_uniform(seed=0))(X)
# model = Model(inputs = X_input, outputs = X, name = 'ResNet50')(X) # 后面一定不要再带（X）
model = Model(inputs = X_input, outputs = X, name='ResNet50')
return model
2.3、PlaceHolder --> model = ResNet50(input_shape=(64,64,3),classes=6)
2.4、选择优化算法和目标：model.compile(optimizer=‘adam’,loss=‘categorical_crossentropy’,metrics= [‘accuracy’])
2.5、流图实例化：model.fit(X_train,Y_train,epochs=2,batch_size=32)
2.6、预测：preds = model(x=X_test,y=Y_test)
Loss = preds[0]、Accuracy = preds[1]
tf.reset_default_graph()
model = ResNet50(input_shape = (64, 64, 3), classes = 6)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy']) # 两种loss区别？
'''
sigmoid与binary_crossentropy，以及softmax与categorical_crossentropy的关系
https://blog.csdn.net/Julialove102123/article/details/80236180
当使用categorical_crossentropy损失函数时，你的标签应为多类模式
'''
model.fit(X_train, Y_train, epochs = 2, batch_size = 32)
preds = model.evaluate(X_test, Y_test)
print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1]))
Epoch 1/2
1080/1080 [==============================] - 151s 139ms/step - loss: 2.0197 - acc: 0.4333
Epoch 2/2
1080/1080 [==============================] - 117s 108ms/step - loss: 0.7461 - acc: 0.7287
120/120 [==============================] - 7s 56ms/step
Loss = 9.794167518615723
Test Accuracy = 0.17500000049670536
2.7、Test on your own image
img_path = 'images/my_image (2).jpg'
my_image = scipy.misc.imread(img_path)
imshow(my_image)
img = image.load_img(img_path, target_size=(64, 64))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
print('Input image shape:', x.shape)
print("class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = ")
print(model.predict(x))
C:\conda\envs\tensorflow\lib\site-packages\ipykernel_launcher.py:2: DeprecationWarning: `imread` is deprecated!
`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.
Use ``imageio.imread`` instead.
Input image shape: (1, 64, 64, 3)
class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] =
[[1. 0. 0. 0. 0. 0.]]
# 加载别人训练好的模型参数
# model = load_model('ResNet50.h5')
model = load_model('resnet50_44_epochs.h5')
preds = model.evaluate(X_test, Y_test)
print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1]))
C:\conda\envs\tensorflow\lib\site-packages\keras\engine\saving.py:327: UserWarning: Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.
warnings.warn('Error in loading the saved optimizer '
120/120 [==============================] - 15s 126ms/step
Loss = 0.09144997075200081
Test Accuracy = 0.9583333373069763
2.8、Print a summary of your model
model.summary()
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_5 (InputLayer) (None, 64, 64, 3) 0
__________________________________________________________________________________________________
zero_padding2d_5 (ZeroPadding2D (None, 70, 70, 3) 0 input_5[0][0]
__________________________________________________________________________________________________
conv1 (Conv2D) (None, 32, 32, 64) 9472 zero_padding2d_5[0][0]
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization) (None, 32, 32, 64) 256 conv1[0][0]
__________________________________________________________________________________________________
activation_200 (Activation) (None, 32, 32, 64) 0 bn_conv1[0][0]
__________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D) (None, 15, 15, 64) 0 activation_200[0][0]
__________________________________________________________________________________________________
res2a_branch2a (Conv2D) (None, 15, 15, 64) 4160 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
bn2a_branch2a (BatchNormalizati (None, 15, 15, 64) 256 res2a_branch2a[0][0]
__________________________________________________________________________________________________
activation_201 (Activation) (None, 15, 15, 64) 0 bn2a_branch2a[0][0]
__________________________________________________________________________________________________
res2a_branch2b (Conv2D) (None, 15, 15, 64) 36928 activation_201[0][0]
__________________________________________________________________________________________________
bn2a_branch2b (BatchNormalizati (None, 15, 15, 64) 256 res2a_branch2b[0][0]
__________________________________________________________________________________________________
activation_202 (Activation) (None, 15, 15, 64) 0 bn2a_branch2b[0][0]
__________________________________________________________________________________________________
res2a_branch2c (Conv2D) (None, 15, 15, 256) 16640 activation_202[0][0]
__________________________________________________________________________________________________
res2a_branch1 (Conv2D) (None, 15, 15, 256) 16640 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
bn2a_branch2c (BatchNormalizati (None, 15, 15, 256) 1024 res2a_branch2c[0][0]
__________________________________________________________________________________________________
bn2a_branch1 (BatchNormalizatio (None, 15, 15, 256) 1024 res2a_branch1[0][0]
__________________________________________________________________________________________________
add_66 (Add) (None, 15, 15, 256) 0 bn2a_branch2c[0][0]
bn2a_branch1[0][0]
__________________________________________________________________________________________________
activation_203 (Activation) (None, 15, 15, 256) 0 add_66[0][0]
__________________________________________________________________________________________________
res2b_branch2a (Conv2D) (None, 15, 15, 64) 16448 activation_203[0][0]
__________________________________________________________________________________________________
bn2b_branch2a (BatchNormalizati (None, 15, 15, 64) 256 res2b_branch2a[0][0]
__________________________________________________________________________________________________
activation_204 (Activation) (None, 15, 15, 64) 0 bn2b_branch2a[0][0]
__________________________________________________________________________________________________
res2b_branch2b (Conv2D) (None, 15, 15, 64) 36928 activation_204[0][0]
__________________________________________________________________________________________________
bn2b_branch2b (BatchNormalizati (None, 15, 15, 64) 256 res2b_branch2b[0][0]
__________________________________________________________________________________________________
activation_205 (Activation) (None, 15, 15, 64) 0 bn2b_branch2b[0][0]
__________________________________________________________________________________________________
res2b_branch2c (Conv2D) (None, 15, 15, 256) 16640 activation_205[0][0]
__________________________________________________________________________________________________
bn2b_branch2c (BatchNormalizati (None, 15, 15, 256) 1024 res2b_branch2c[0][0]
__________________________________________________________________________________________________
add_67 (Add) (None, 15, 15, 256) 0 bn2b_branch2c[0][0]
activation_203[0][0]
__________________________________________________________________________________________________
activation_206 (Activation) (None, 15, 15, 256) 0 add_67[0][0]
__________________________________________________________________________________________________
res2c_branch2a (Conv2D) (None, 15, 15, 64) 16448 activation_206[0][0]
__________________________________________________________________________________________________
bn2c_branch2a (BatchNormalizati (None, 15, 15, 64) 256 res2c_branch2a[0][0]
__________________________________________________________________________________________________
activation_207 (Activation) (None, 15, 15, 64) 0 bn2c_branch2a[0][0]
__________________________________________________________________________________________________
res2c_branch2b (Conv2D) (None, 15, 15, 64) 36928 activation_207[0][0]
__________________________________________________________________________________________________
bn2c_branch2b (BatchNormalizati (None, 15, 15, 64) 256 res2c_branch2b[0][0]
__________________________________________________________________________________________________
activation_208 (Activation) (None, 15, 15, 64) 0 bn2c_branch2b[0][0]
__________________________________________________________________________________________________
res2c_branch2c (Conv2D) (None, 15, 15, 256) 16640 activation_208[0][0]
__________________________________________________________________________________________________
bn2c_branch2c (BatchNormalizati (None, 15, 15, 256) 1024 res2c_branch2c[0][0]
__________________________________________________________________________________________________
add_68 (Add) (None, 15, 15, 256) 0 bn2c_branch2c[0][0]
activation_206[0][0]
__________________________________________________________________________________________________
activation_209 (Activation) (None, 15, 15, 256) 0 add_68[0][0]
__________________________________________________________________________________________________
res3a_branch2a (Conv2D) (None, 8, 8, 128) 32896 activation_209[0][0]
__________________________________________________________________________________________________
bn3a_branch2a (BatchNormalizati (None, 8, 8, 128) 512 res3a_branch2a[0][0]
__________________________________________________________________________________________________
activation_210 (Activation) (None, 8, 8, 128) 0 bn3a_branch2a[0][0]
__________________________________________________________________________________________________
res3a_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_210[0][0]
__________________________________________________________________________________________________
bn3a_branch2b (BatchNormalizati (None, 8, 8, 128) 512 res3a_branch2b[0][0]
__________________________________________________________________________________________________
activation_211 (Activation) (None, 8, 8, 128) 0 bn3a_branch2b[0][0]
__________________________________________________________________________________________________
res3a_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_211[0][0]
__________________________________________________________________________________________________
res3a_branch1 (Conv2D) (None, 8, 8, 512) 131584 activation_209[0][0]
__________________________________________________________________________________________________
bn3a_branch2c (BatchNormalizati (None, 8, 8, 512) 2048 res3a_branch2c[0][0]
__________________________________________________________________________________________________
bn3a_branch1 (BatchNormalizatio (None, 8, 8, 512) 2048 res3a_branch1[0][0]
__________________________________________________________________________________________________
add_69 (Add) (None, 8, 8, 512) 0 bn3a_branch2c[0][0]
bn3a_branch1[0][0]
__________________________________________________________________________________________________
activation_212 (Activation) (None, 8, 8, 512) 0 add_69[0][0]
__________________________________________________________________________________________________
res3b_branch2a (Conv2D) (None, 8, 8, 128) 65664 activation_212[0][0]
__________________________________________________________________________________________________
bn3b_branch2a (BatchNormalizati (None, 8, 8, 128) 512 res3b_branch2a[0][0]
__________________________________________________________________________________________________
activation_213 (Activation) (None, 8, 8, 128) 0 bn3b_branch2a[0][0]
__________________________________________________________________________________________________
res3b_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_213[0][0]
__________________________________________________________________________________________________
bn3b_branch2b (BatchNormalizati (None, 8, 8, 128) 512 res3b_branch2b[0][0]
__________________________________________________________________________________________________
activation_214 (Activation) (None, 8, 8, 128) 0 bn3b_branch2b[0][0]
__________________________________________________________________________________________________
res3b_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_214[0][0]
__________________________________________________________________________________________________
bn3b_branch2c (BatchNormalizati (None, 8, 8, 512) 2048 res3b_branch2c[0][0]
__________________________________________________________________________________________________
add_70 (Add) (None, 8, 8, 512) 0 bn3b_branch2c[0][0]
activation_212[0][0]
__________________________________________________________________________________________________
activation_215 (Activation) (None, 8, 8, 512) 0 add_70[0][0]
__________________________________________________________________________________________________
res3c_branch2a (Conv2D) (None, 8, 8, 128) 65664 activation_215[0][0]
__________________________________________________________________________________________________
bn3c_branch2a (BatchNormalizati (None, 8, 8, 128) 512 res3c_branch2a[0][0]
__________________________________________________________________________________________________
activation_216 (Activation) (None, 8, 8, 128) 0 bn3c_branch2a[0][0]
__________________________________________________________________________________________________
res3c_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_216[0][0]
__________________________________________________________________________________________________
bn3c_branch2b (BatchNormalizati (None, 8, 8, 128) 512 res3c_branch2b[0][0]
__________________________________________________________________________________________________
activation_217 (Activation) (None, 8, 8, 128) 0 bn3c_branch2b[0][0]
__________________________________________________________________________________________________
res3c_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_217[0][0]
__________________________________________________________________________________________________
bn3c_branch2c (BatchNormalizati (None, 8, 8, 512) 2048 res3c_branch2c[0][0]
__________________________________________________________________________________________________
add_71 (Add) (None, 8, 8, 512) 0 bn3c_branch2c[0][0]
activation_215[0][0]
__________________________________________________________________________________________________
activation_218 (Activation) (None, 8, 8, 512) 0 add_71[0][0]
__________________________________________________________________________________________________
res3d_branch2a (Conv2D) (None, 8, 8, 128) 65664 activation_218[0][0]
__________________________________________________________________________________________________
bn3d_branch2a (BatchNormalizati (None, 8, 8, 128) 512 res3d_branch2a[0][0]
__________________________________________________________________________________________________
activation_219 (Activation) (None, 8, 8, 128) 0 bn3d_branch2a[0][0]
__________________________________________________________________________________________________
res3d_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_219[0][0]
__________________________________________________________________________________________________
bn3d_branch2b (BatchNormalizati (None, 8, 8, 128) 512 res3d_branch2b[0][0]
__________________________________________________________________________________________________
activation_220 (Activation) (None, 8, 8, 128) 0 bn3d_branch2b[0][0]
__________________________________________________________________________________________________
res3d_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_220[0][0]
__________________________________________________________________________________________________
bn3d_branch2c (BatchNormalizati (None, 8, 8, 512) 2048 res3d_branch2c[0][0]
__________________________________________________________________________________________________
add_72 (Add) (None, 8, 8, 512) 0 bn3d_branch2c[0][0]
activation_218[0][0]
__________________________________________________________________________________________________
activation_221 (Activation) (None, 8, 8, 512) 0 add_72[0][0]
__________________________________________________________________________________________________
res4a_branch2a (Conv2D) (None, 4, 4, 256) 131328 activation_221[0][0]
__________________________________________________________________________________________________
bn4a_branch2a (BatchNormalizati (None, 4, 4, 256) 1024 res4a_branch2a[0][0]
__________________________________________________________________________________________________
activation_222 (Activation) (None, 4, 4, 256) 0 bn4a_branch2a[0][0]
__________________________________________________________________________________________________
res4a_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_222[0][0]
__________________________________________________________________________________________________
bn4a_branch2b (BatchNormalizati (None, 4, 4, 256) 1024 res4a_branch2b[0][0]
__________________________________________________________________________________________________
activation_223 (Activation) (None, 4, 4, 256) 0 bn4a_branch2b[0][0]
__________________________________________________________________________________________________
res4a_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_223[0][0]
__________________________________________________________________________________________________
res4a_branch1 (Conv2D) (None, 4, 4, 1024) 525312 activation_221[0][0]
__________________________________________________________________________________________________
bn4a_branch2c (BatchNormalizati (None, 4, 4, 1024) 4096 res4a_branch2c[0][0]
__________________________________________________________________________________________________
bn4a_branch1 (BatchNormalizatio (None, 4, 4, 1024) 4096 res4a_branch1[0][0]
__________________________________________________________________________________________________
add_73 (Add) (None, 4, 4, 1024) 0 bn4a_branch2c[0][0]
bn4a_branch1[0][0]
__________________________________________________________________________________________________
activation_224 (Activation) (None, 4, 4, 1024) 0 add_73[0][0]
__________________________________________________________________________________________________
res4b_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_224[0][0]
__________________________________________________________________________________________________
bn4b_branch2a (BatchNormalizati (None, 4, 4, 256) 1024 res4b_branch2a[0][0]
__________________________________________________________________________________________________
activation_225 (Activation) (None, 4, 4, 256) 0 bn4b_branch2a[0][0]
__________________________________________________________________________________________________
res4b_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_225[0][0]
__________________________________________________________________________________________________
bn4b_branch2b (BatchNormalizati (None, 4, 4, 256) 1024 res4b_branch2b[0][0]
__________________________________________________________________________________________________
activation_226 (Activation) (None, 4, 4, 256) 0 bn4b_branch2b[0][0]
__________________________________________________________________________________________________
res4b_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_226[0][0]
__________________________________________________________________________________________________
bn4b_branch2c (BatchNormalizati (None, 4, 4, 1024) 4096 res4b_branch2c[0][0]
__________________________________________________________________________________________________
add_74 (Add) (None, 4, 4, 1024) 0 bn4b_branch2c[0][0]
activation_224[0][0]
__________________________________________________________________________________________________
activation_227 (Activation) (None, 4, 4, 1024) 0 add_74[0][0]
__________________________________________________________________________________________________
res4c_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_227[0][0]
__________________________________________________________________________________________________
bn4c_branch2a (BatchNormalizati (None, 4, 4, 256) 1024 res4c_branch2a[0][0]
__________________________________________________________________________________________________
activation_228 (Activation) (None, 4, 4, 256) 0 bn4c_branch2a[0][0]
__________________________________________________________________________________________________
res4c_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_228[0][0]
__________________________________________________________________________________________________
bn4c_branch2b (BatchNormalizati (None, 4, 4, 256) 1024 res4c_branch2b[0][0]
__________________________________________________________________________________________________
activation_229 (Activation) (None, 4, 4, 256) 0 bn4c_branch2b[0][0]
__________________________________________________________________________________________________
res4c_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_229[0][0]
__________________________________________________________________________________________________
bn4c_branch2c (BatchNormalizati (None, 4, 4, 1024) 4096 res4c_branch2c[0][0]
__________________________________________________________________________________________________
add_75 (Add) (None, 4, 4, 1024) 0 bn4c_branch2c[0][0]
activation_227[0][0]
__________________________________________________________________________________________________
activation_230 (Activation) (None, 4, 4, 1024) 0 add_75[0][0]
__________________________________________________________________________________________________
res4d_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_230[0][0]
__________________________________________________________________________________________________
bn4d_branch2a (BatchNormalizati (None, 4, 4, 256) 1024 res4d_branch2a[0][0]
__________________________________________________________________________________________________
activation_231 (Activation) (None, 4, 4, 256) 0 bn4d_branch2a[0][0]
__________________________________________________________________________________________________
res4d_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_231[0][0]
__________________________________________________________________________________________________
bn4d_branch2b (BatchNormalizati (None, 4, 4, 256) 1024 res4d_branch2b[0][0]
__________________________________________________________________________________________________
activation_232 (Activation) (None, 4, 4, 256) 0 bn4d_branch2b[0][0]
__________________________________________________________________________________________________
res4d_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_232[0][0]
__________________________________________________________________________________________________
bn4d_branch2c (BatchNormalizati (None, 4, 4, 1024) 4096 res4d_branch2c[0][0]
__________________________________________________________________________________________________
add_76 (Add) (None, 4, 4, 1024) 0 bn4d_branch2c[0][0]
activation_230[0][0]
__________________________________________________________________________________________________
activation_233 (Activation) (None, 4, 4, 1024) 0 add_76[0][0]
__________________________________________________________________________________________________
res4e_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_233[0][0]
__________________________________________________________________________________________________
bn4e_branch2a (BatchNormalizati (None, 4, 4, 256) 1024 res4e_branch2a[0][0]
__________________________________________________________________________________________________
activation_234 (Activation) (None, 4, 4, 256) 0 bn4e_branch2a[0][0]
__________________________________________________________________________________________________
res4e_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_234[0][0]
__________________________________________________________________________________________________
bn4e_branch2b (BatchNormalizati (None, 4, 4, 256) 1024 res4e_branch2b[0][0]
__________________________________________________________________________________________________
activation_235 (Activation) (None, 4, 4, 256) 0 bn4e_branch2b[0][0]
__________________________________________________________________________________________________
res4e_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_235[0][0]
__________________________________________________________________________________________________
bn4e_branch2c (BatchNormalizati (None, 4, 4, 1024) 4096 res4e_branch2c[0][0]
__________________________________________________________________________________________________
add_77 (Add) (None, 4, 4, 1024) 0 bn4e_branch2c[0][0]
activation_233[0][0]
__________________________________________________________________________________________________
activation_236 (Activation) (None, 4, 4, 1024) 0 add_77[0][0]
__________________________________________________________________________________________________
res4f_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_236[0][0]
__________________________________________________________________________________________________
bn4f_branch2a (BatchNormalizati (None, 4, 4, 256) 1024 res4f_branch2a[0][0]
__________________________________________________________________________________________________
activation_237 (Activation) (None, 4, 4, 256) 0 bn4f_branch2a[0][0]
__________________________________________________________________________________________________
res4f_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_237[0][0]
__________________________________________________________________________________________________
bn4f_branch2b (BatchNormalizati (None, 4, 4, 256) 1024 res4f_branch2b[0][0]
__________________________________________________________________________________________________
activation_238 (Activation) (None, 4, 4, 256) 0 bn4f_branch2b[0][0]
__________________________________________________________________________________________________
res4f_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_238[0][0]
__________________________________________________________________________________________________
bn4f_branch2c (BatchNormalizati (None, 4, 4, 1024) 4096 res4f_branch2c[0][0]
__________________________________________________________________________________________________
add_78 (Add) (None, 4, 4, 1024) 0 bn4f_branch2c[0][0]
activation_236[0][0]
__________________________________________________________________________________________________
activation_239 (Activation) (None, 4, 4, 1024) 0 add_78[0][0]
__________________________________________________________________________________________________
res5a_branch2a (Conv2D) (None, 2, 2, 512) 524800 activation_239[0][0]
__________________________________________________________________________________________________
bn5a_branch2a (BatchNormalizati (None, 2, 2, 512) 2048 res5a_branch2a[0][0]
__________________________________________________________________________________________________
activation_240 (Activation) (None, 2, 2, 512) 0 bn5a_branch2a[0][0]
__________________________________________________________________________________________________
res5a_branch2b (Conv2D) (None, 2, 2, 512) 2359808 activation_240[0][0]
__________________________________________________________________________________________________
bn5a_branch2b (BatchNormalizati (None, 2, 2, 512) 2048 res5a_branch2b[0][0]
__________________________________________________________________________________________________
activation_241 (Activation) (None, 2, 2, 512) 0 bn5a_branch2b[0][0]
__________________________________________________________________________________________________
res5a_branch2c (Conv2D) (None, 2, 2, 2048) 1050624 activation_241[0][0]
__________________________________________________________________________________________________
res5a_branch1 (Conv2D) (None, 2, 2, 2048) 2099200 activation_239[0][0]
__________________________________________________________________________________________________
bn5a_branch2c (BatchNormalizati (None, 2, 2, 2048) 8192 res5a_branch2c[0][0]
__________________________________________________________________________________________________
bn5a_branch1 (BatchNormalizatio (None, 2, 2, 2048) 8192 res5a_branch1[0][0]
__________________________________________________________________________________________________
add_79 (Add) (None, 2, 2, 2048) 0 bn5a_branch2c[0][0]
bn5a_branch1[0][0]
__________________________________________________________________________________________________
activation_242 (Activation) (None, 2, 2, 2048) 0 add_79[0][0]
__________________________________________________________________________________________________
res5b_branch2a (Conv2D) (None, 2, 2, 512) 1049088 activation_242[0][0]
__________________________________________________________________________________________________
bn5b_branch2a (BatchNormalizati (None, 2, 2, 512) 2048 res5b_branch2a[0][0]
__________________________________________________________________________________________________
activation_243 (Activation) (None, 2, 2, 512) 0 bn5b_branch2a[0][0]
__________________________________________________________________________________________________
res5b_branch2b (Conv2D) (None, 2, 2, 512) 2359808 activation_243[0][0]
__________________________________________________________________________________________________
bn5b_branch2b (BatchNormalizati (None, 2, 2, 512) 2048 res5b_branch2b[0][0]
__________________________________________________________________________________________________
activation_244 (Activation) (None, 2, 2, 512) 0 bn5b_branch2b[0][0]
__________________________________________________________________________________________________
res5b_branch2c (Conv2D) (None, 2, 2, 2048) 1050624 activation_244[0][0]
__________________________________________________________________________________________________
bn5b_branch2c (BatchNormalizati (None, 2, 2, 2048) 8192 res5b_branch2c[0][0]
__________________________________________________________________________________________________
add_80 (Add) (None, 2, 2, 2048) 0 bn5b_branch2c[0][0]
activation_242[0][0]
__________________________________________________________________________________________________
activation_245 (Activation) (None, 2, 2, 2048) 0 add_80[0][0]
__________________________________________________________________________________________________
res5c_branch2a (Conv2D) (None, 2, 2, 512) 1049088 activation_245[0][0]
__________________________________________________________________________________________________
bn5c_branch2a (BatchNormalizati (None, 2, 2, 512) 2048 res5c_branch2a[0][0]
__________________________________________________________________________________________________
activation_246 (Activation) (None, 2, 2, 512) 0 bn5c_branch2a[0][0]
__________________________________________________________________________________________________
res5c_branch2b (Conv2D) (None, 2, 2, 512) 2359808 activation_246[0][0]
__________________________________________________________________________________________________
bn5c_branch2b (BatchNormalizati (None, 2, 2, 512) 2048 res5c_branch2b[0][0]
__________________________________________________________________________________________________
activation_247 (Activation) (None, 2, 2, 512) 0 bn5c_branch2b[0][0]
__________________________________________________________________________________________________
res5c_branch2c (Conv2D) (None, 2, 2, 2048) 1050624 activation_247[0][0]
__________________________________________________________________________________________________
bn5c_branch2c (BatchNormalizati (None, 2, 2, 2048) 8192 res5c_branch2c[0][0]
__________________________________________________________________________________________________
add_81 (Add) (None, 2, 2, 2048) 0 bn5c_branch2c[0][0]
activation_245[0][0]
__________________________________________________________________________________________________
activation_248 (Activation) (None, 2, 2, 2048) 0 add_81[0][0]
__________________________________________________________________________________________________
avg_pool (AveragePooling2D) (None, 1, 1, 2048) 0 activation_248[0][0]
__________________________________________________________________________________________________
flatten_5 (Flatten) (None, 2048) 0 avg_pool[0][0]
__________________________________________________________________________________________________
fc6 (Dense) (None, 6) 12294 flatten_5[0][0]
==================================================================================================
Total params: 23,600,006
Trainable params: 23,546,886
Non-trainable params: 53,120
__________________________________________________________________________________________________
plot_model(model, to_file='model.png')
SVG(model_to_dot(model).create(prog='dot', format='svg'))
