来自吴恩达深度学习系列视频 卷积神经网络 第三周作业：Residual+Networks±+v1，它使用Keras实现了著名ResNet50。中文翻译参照：【中文】【吴恩达课后编程作业】Course 4 - 卷积神经网络 - 第二周作业 - Keras入门与残差网络的搭建 参照对代码的解释并非完全准确，这点还请注意：
完整的ipynb文件参见博主github：https://github.com/Hongze-Wang/Deep-Learning-Andrew-Ng/tree/master/homework
Residual Networks
Welcome to the second assignment of this week! You will learn how to build very deep convolutional networks, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, they are hard to train. Residual Networks, introduced by He et al., allow you to train much deeper networks than were previously practically feasible.
In this assignment, you will:
- Implement the basic building blocks of ResNets.
- Put together these building blocks to implement and train a state-of-the-art neural network for image classification.
This assignment will be done in Keras.
Before jumping into the problem, let’s run the cell below to load the required packages.
import numpy as np
import tensorflow as tf
from keras import layers
from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D
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 resnets_utils import *
from keras.initializers import glorot_uniform
import scipy.misc
from matplotlib.pyplot import imshow
%matplotlib inline
import keras.backend as K
K.set_image_data_format('channels_last')
K.set_learning_phase(1)
Using TensorFlow backend.
1 - The problem of very deep neural networks
Last week, you built your first convolutional neural network. In recent years, neural networks have become deeper, with state-of-the-art networks going from just a few layers (e.g., AlexNet) to over a hundred layers.
The main benefit of a very deep network is that it can represent very complex functions. It can also learn features at many different levels of abstraction, from edges (at the lower layers) to very complex features (at the deeper layers). However, using a deeper network doesn’t always help. A huge barrier to training them is vanishing gradients: very deep networks often have a gradient signal that goes to zero quickly, thus making gradient descent unbearably slow. More specifically, during gradient descent, as you backprop from the final layer back to the first layer, you are multiplying by the weight matrix on each step, and thus the gradient can decrease exponentially quickly to zero (or, in rare cases, grow exponentially quickly and “explode” to take very large values).
During training, you might therefore see the magnitude (or norm) of the gradient for the earlier layers descrease to zero very rapidly as training proceeds:
The speed of learning decreases very rapidly for the early layers as the network trains
You are now going to solve this problem by building a Residual Network!
2 - Building a Residual Network
In ResNets, a “shortcut” or a “skip connection” allows the gradient to be directly backpropagated to earlier layers:
The image on the left shows the “main path” through the network. The image on the right adds a shortcut to the main path. By stacking these ResNet blocks on top of each other, you can form a very deep network.
We also saw in lecture that having ResNet blocks with the shortcut also makes it very easy for one of the blocks to learn an identity function. This means that you can stack on additional ResNet blocks with little risk of harming training set performance. (There is also some evidence that the ease of learning an identity function–even more than skip connections helping with vanishing gradients–accounts for ResNets’ remarkable performance.)
Two main types of blocks are used in a ResNet, depending mainly on whether the input/output dimensions are same or different. You are going to implement both of them.
2.1 - The identity block
The identity block is the standard block used in ResNets, and corresponds to the case where the input activation (say
a
[
l
]
a^{[l]}
a[l]) has the same dimension as the output activation (say
a
[
l
+
2
]
a^{[l+2]}
a[l+2]). To flesh out the different steps of what happens in a ResNet’s identity block, here is an alternative diagram showing the individual steps:
The upper path is the “shortcut path.” The lower path is the “main path.” In this diagram, we have also made explicit the CONV2D and ReLU steps in each layer. To speed up training we have also added a BatchNorm step. Don’t worry about this being complicated to implement–you’ll see that BatchNorm is just one line of code in Keras!
In this exercise, you’ll actually implement a slightly more powerful version of this identity block, in which the skip connection “skips over” 3 hidden layers rather than 2 layers. It looks like this:
Here’re the individual steps.
First component of main path:
- The first CONV2D has
F
1
F_1
F1 filters of shape (1,1) and a stride of (1,1). Its padding is “valid” and its name should be
conv_name_base + '2a'. Use 0 as the seed for the random initialization. - The first BatchNorm is normalizing the channels axis. Its name should be
bn_name_base + '2a'. - Then apply the ReLU activation function. This has no name and no hyperparameters.
Second component of main path:
- The second CONV2D has
F
2
F_2
F2 filters of shape
(
f
,
f
)
(f,f)
(f,f) and a stride of (1,1). Its padding is “same” and its name should be
conv_name_base + '2b'. Use 0 as the seed for the random initialization. - The second BatchNorm is normalizing the channels axis. Its name should be
bn_name_base + '2b'. - Then apply the ReLU activation function. This has no name and no hyperparameters.
Third component of main path:
- The third CONV2D has
F
3
F_3
F3 filters of shape (1,1) and a stride of (1,1). Its padding is “valid” and its name should be
conv_name_base + '2c'. Use 0 as the seed for the random initialization. - The third BatchNorm is normalizing the channels axis. Its name should be
bn_name_base + '2c'. Note that there is no ReLU activation function in this component.
Final step:
- The shortcut and the input are added together.
- Then apply the ReLU activation function. This has no name and no hyperparameters.
Exercise: Implement the ResNet identity block. We have implemented the first component of the main path. Please read over this carefully to make sure you understand what it is doing. You should implement the rest.
- To implement the Conv2D step: See reference
- To implement BatchNorm: See reference (axis: Integer, the axis that should be normalized (typically the channels axis))
- For the activation, use:
Activation('relu')(X) - To add the value passed forward by the shortcut: See reference
# GRADED FUNCTION: identity_block
def identity_block(X, f, filters, stage, block):
"""
Implementation of the identity block as defined in Figure 4
Arguments:
X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
f -- integer, specifying the shape of the middle CONV's window for the main path
filters -- python list of integers, defining the number of filters in the CONV layers of the main path
stage -- integer, used to name the layers, depending on their position in the network
block -- string/character, used to name the layers, depending on their position in the network
Returns:
X -- output of the identity block, tensor of shape (n_H, n_W, n_C)
"""
# defining name basis
conv_name_base = "res" + str(stage) + block + "_branch"
bn_name_base = "bn" + str(stage) + block + "_branch"
# Retrieve Filters
F1, F2, F3 = filters
# Save the input value. You'll need this later to add back to the main path.
X_shortcut = X
# First component of main path
X = Conv2D(filters=F1, kernel_size=(1, 1), strides=(1, 1), padding="valid",
name=conv_name_base+"2a", kernel_initializer=glorot_uniform(seed=0))(X)
#valid mean no padding / glorot_uniform equal to Xaiver initialization - Steve
X = BatchNormalization(axis=3, name=bn_name_base + "2a")(X)
X = Activation("relu")(X)
### START CODE HERE ###
# Second component of main path (≈3 lines)
X = Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding="same",
name=conv_name_base+"2b", kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base+"2b")(X)
X = Activation("relu")(X)
# Third component of main path (≈2 lines)
# Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)
X = Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding="valid",
name=conv_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)
### END CODE HERE ###
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 = [0.19716819 0. 1.3561226 2.1713073 0. 1.3324987 ]
2.2 - The convolutional block
You’ve implemented the ResNet identity block. Next, the ResNet “convolutional block” is the other type of block. You can use this type of block when the input and output dimensions don’t match up. The difference with the identity block is that there is a CONV2D layer in the shortcut path:
The CONV2D layer in the shortcut path is used to resize the input x x x to a different dimension, so that the dimensions match up in the final addition needed to add the shortcut value back to the main path. (This plays a similar role as the matrix W s W_s Ws discussed in lecture.) For example, to reduce the activation dimensions’s height and width by a factor of 2, you can use a 1x1 convolution with a stride of 2. The CONV2D layer on the shortcut path does not use any non-linear activation function. Its main role is to just apply a (learned) linear function that reduces the dimension of the input, so that the dimensions match up for the later addition step.
The details of the convolutional block are as follows.
First component of main path:
- The first CONV2D has
F
1
F_1
F1 filters of shape (1,1) and a stride of (s,s). Its padding is “valid” and its name should be
conv_name_base + '2a'. - The first BatchNorm is normalizing the channels axis. Its name should be
bn_name_base + '2a'. - Then apply the ReLU activation function. This has no name and no hyperparameters.
Second component of main path:
- The second CONV2D has
F
2
F_2
F2 filters of (f,f) and a stride of (1,1). Its padding is “same” and it’s name should be
conv_name_base + '2b'. - The second BatchNorm is normalizing the channels axis. Its name should be
bn_name_base + '2b'. - Then apply the ReLU activation function. This has no name and no hyperparameters.
Third component of main path:
- The third CONV2D has
F
3
F_3
F3 filters of (1,1) and a stride of (1,1). Its padding is “valid” and it’s name should be
conv_name_base + '2c'. - The third BatchNorm is normalizing the channels axis. Its name should be
bn_name_base + '2c'. Note that there is no ReLU activation function in this component.
Shortcut path:
- The CONV2D has
F
3
F_3
F3 filters of shape (1,1) and a stride of (s,s). Its padding is “valid” and its name should be
conv_name_base + '1'. - The BatchNorm is normalizing the channels axis. Its name should be
bn_name_base + '1'.
Final step:
- The shortcut and the main path values are added together.
- Then apply the ReLU activation function. This has no name and no hyperparameters.
Exercise: Implement the convolutional block. We have implemented the first component of the main path; you should implement the rest. As before, always use 0 as the seed for the random initialization, to ensure consistency with our grader.
- Conv Hint
- BatchNorm Hint (axis: Integer, the axis that should be normalized (typically the features axis))
- For the activation, use:
Activation('relu')(X) - Addition Hint
# GRADED FUNCTION: convolutional_block
def convolutional_block(X, f, filters, stage, block, s = 2):
"""
Implementation of the convolutional block as defined in Figure 4
Arguments:
X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
f -- integer, specifying the shape of the middle CONV's window for the main path
filters -- python list of integers, defining the number of filters in the CONV layers of the main path
stage -- integer, used to name the layers, depending on their position in the network
block -- string/character, used to name the layers, depending on their position in the network
s -- Integer, specifying the stride to be used
Returns:
X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C)
"""
# defining name basis
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
# Retrieve Filters
F1, F2, F3 = filters
# Save the input value
X_shortcut = X
##### MAIN PATH #####
# First component of main path
X = Conv2D(F1, (1, 1), strides = (s,s), name = conv_name_base + '2a', padding='valid', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)
X = Activation('relu')(X)
### START CODE HERE ###
# Second component of main path (≈3 lines)
X = Conv2D(F2, (f, f), strides = (1, 1), name = conv_name_base + '2b',padding='same', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)
X = Activation('relu')(X)
# Third component of main path (≈2 lines)
X = Conv2D(F3, (1, 1), strides = (1, 1), name = conv_name_base + '2c',padding='valid', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)
##### SHORTCUT PATH #### (≈2 lines)
X_shortcut = Conv2D(F3, (1, 1), strides = (s, s), name = conv_name_base + '1',padding='valid', kernel_initializer = glorot_uniform(seed=0))(X_shortcut)
X_shortcut = BatchNormalization(axis = 3, name = bn_name_base + '1')(X_shortcut)
# Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)
X = layers.add([X, X_shortcut])
X = Activation('relu')(X)
### END CODE HERE ###
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 = [0.09018463 1.2348979 0.46822023 0.03671762 0. 0.65516603]
3 - Building your first ResNet model (50 layers)
You now have the necessary blocks to build a very deep ResNet. The following figure describes in detail the architecture of this neural network. “ID BLOCK” in the diagram stands for “Identity block,” and “ID BLOCK x3” means you should stack 3 identity blocks together.
The details of this ResNet-50 model are:
- Zero-padding pads the input with a pad of (3,3)
- Stage 1:
- The 2D Convolution has 64 filters of shape (7,7) and uses a stride of (2,2). Its name is “conv1”.
- BatchNorm is applied to the channels axis of the input.
- MaxPooling uses a (3,3) window and a (2,2) stride.
- Stage 2:
- The convolutional block uses three set of filters of size [64,64,256], “f” is 3, “s” is 1 and the block is “a”.
- The 2 identity blocks use three set of filters of size [64,64,256], “f” is 3 and the blocks are “b” and “c”.
- Stage 3:
- The convolutional block uses three set of filters of size [128,128,512], “f” is 3, “s” is 2 and the block is “a”.
- The 3 identity blocks use three set of filters of size [128,128,512], “f” is 3 and the blocks are “b”, “c” and “d”.
- Stage 4:
- The convolutional block uses three set of filters of size [256, 256, 1024], “f” is 3, “s” is 2 and the block is “a”.
- The 5 identity blocks use three set of filters of size [256, 256, 1024], “f” is 3 and the blocks are “b”, “c”, “d”, “e” and “f”.
- Stage 5:
- The convolutional block uses three set of filters of size [512, 512, 2048], “f” is 3, “s” is 2 and the block is “a”.
- The 2 identity blocks use three set of filters of size [256, 256, 2048], “f” is 3 and the blocks are “b” and “c”.
- The 2D Average Pooling uses a window of shape (2,2) and its name is “avg_pool”.
- The flatten doesn’t have any hyperparameters or name.
- The Fully Connected (Dense) layer reduces its input to the number of classes using a softmax activation. Its name should be
'fc' + str(classes).
Exercise: Implement the ResNet with 50 layers described in the figure above. We have implemented Stages 1 and 2. Please implement the rest. (The syntax for implementing Stages 3-5 should be quite similar to that of Stage 2.) Make sure you follow the naming convention in the text above.
You’ll need to use this function:
- Average pooling see reference
Here’re some other functions we used in the code below:
- Conv2D: See reference
- BatchNorm: See reference (axis: Integer, the axis that should be normalized (typically the features axis))
- Zero padding: See reference
- Max pooling: See reference
- Fully conected layer: See reference
- Addition: See reference
# GRADED FUNCTION: ResNet50
def ResNet50(input_shape = (64, 64, 3), classes = 6):
"""
Implementation of the popular ResNet50 the following architecture:
CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3
-> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER
Arguments:
input_shape -- shape of the images of the dataset
classes -- integer, number of classes
Returns:
model -- a Model() instance in Keras
"""
# Define the input as a tensor with shape input_shape
X_input = Input(input_shape)
# Zero-Padding
X = ZeroPadding2D((3, 3))(X_input)
# Stage 1
X = Conv2D(filters=64, kernel_size=(7, 7), strides=(2, 2), name="conv",
kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name="bn_conv1")(X)
X = Activation("relu")(X)
X = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(X)
# Stage 2
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")
### START CODE HERE ###
# Stage 3 (≈4 lines)
# The convolutional block uses three set of filters of size [128,128,512], "f" is 3, "s" is 2 and the block is "a".
# The 3 identity blocks use three set of filters of size [128,128,512], "f" is 3 and the blocks are "b", "c" and "d".
X = convolutional_block(X, f=3, filters=[128, 128, 512], stage=3, block="a", s=1)
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")
# Stage 4 (≈6 lines)
# The convolutional block uses three set of filters of size [256, 256, 1024], "f" is 3, "s" is 2 and the block is "a".
# The 5 identity blocks use three set of filters of size [256, 256, 1024], "f" is 3 and the blocks are "b", "c", "d", "e" and "f".
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") #if you remove this block, you can get a accuracy at 96.666667% better than have this block
# Stage 5 (≈3 lines)
# The convolutional block uses three set of filters of size [512, 512, 2048], "f" is 3, "s" is 2 and the block is "a".
# The 2 identity blocks use three set of filters of size [256, 256, 2048], "f" is 3 and the blocks are "b" and "c".
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")
# filters should be [256, 256, 2048], but it fail to be graded. Use [512, 512, 2048] to pass the grading
# AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)"
# The 2D Average Pooling uses a window of shape (2,2) and its name is "avg_pool".
X = AveragePooling2D(pool_size=(2, 2), padding="same")(X)
### END CODE HERE ###
# output layer
X = Flatten()(X)
X = Dense(classes, activation="softmax", name="fc"+str(classes), kernel_initializer=glorot_uniform(seed=0))(X)
# Create model
model = Model(inputs=X_input, outputs=X, name="ResNet50")
return model
Run the following code to build the model’s graph. If your implementation is not correct you will know it by checking your accuracy when running model.fit(...) below.
model = ResNet50(input_shape=(64, 64, 3), classes=6)
As seen in the Keras Tutorial Notebook, prior training a model, you need to configure the learning process by compiling the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
The model is now ready to be trained. The only thing you need is a dataset.
Let’s load the SIGNS 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.
"""
def convert_to_one_hot(Y, C):
Y = np.eye(C)[Y.reshape(-1)].T
return Y
"""
# 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)
Run the following cell to train your model on 2 epochs with a batch size of 32. On a CPU it should take you around 5min per epoch.
model.fit(X_train, Y_train, epochs = 20, batch_size = 32)
Epoch 1/20
1080/1080 [==============================] - 278s 258ms/step - loss: 3.0214 - acc: 0.3046
Epoch 2/20
1080/1080 [==============================] - 261s 242ms/step - loss: 1.8221 - acc: 0.3611
Epoch 3/20
1080/1080 [==============================] - 270s 250ms/step - loss: 1.4616 - acc: 0.4306
Epoch 4/20
1080/1080 [==============================] - 276s 256ms/step - loss: 1.2550 - acc: 0.4972
Epoch 5/20
1080/1080 [==============================] - 284s 263ms/step - loss: 1.1114 - acc: 0.5611
Epoch 6/20
1080/1080 [==============================] - 279s 258ms/step - loss: 0.9247 - acc: 0.6509
Epoch 7/20
1080/1080 [==============================] - 274s 253ms/step - loss: 0.5469 - acc: 0.8065
Epoch 8/20
1080/1080 [==============================] - 277s 257ms/step - loss: 0.3859 - acc: 0.8620
Epoch 9/20
1080/1080 [==============================] - 289s 267ms/step - loss: 0.3628 - acc: 0.8870
Epoch 10/20
1080/1080 [==============================] - 276s 255ms/step - loss: 0.1572 - acc: 0.9380
Epoch 11/20
1080/1080 [==============================] - 271s 251ms/step - loss: 0.0931 - acc: 0.9667
Epoch 12/20
1080/1080 [==============================] - 277s 256ms/step - loss: 0.0910 - acc: 0.9685
Epoch 13/20
1080/1080 [==============================] - 283s 262ms/step - loss: 0.0692 - acc: 0.9778
Epoch 14/20
1080/1080 [==============================] - 281s 261ms/step - loss: 0.0809 - acc: 0.9806
Epoch 15/20
1080/1080 [==============================] - 257s 238ms/step - loss: 0.0564 - acc: 0.9824
Epoch 16/20
1080/1080 [==============================] - 252s 233ms/step - loss: 0.0590 - acc: 0.9769
Epoch 17/20
1080/1080 [==============================] - 252s 233ms/step - loss: 0.0244 - acc: 0.9917
Epoch 18/20
1080/1080 [==============================] - 252s 233ms/step - loss: 0.0161 - acc: 0.9981
Epoch 19/20
1080/1080 [==============================] - 252s 233ms/step - loss: 0.0047 - acc: 1.0000
Epoch 20/20
1080/1080 [==============================] - 252s 233ms/step - loss: 0.0018 - acc: 1.0000
Let’s see how this model (trained on only two epochs) performs on the test set.
preds = model.evaluate(X_test, Y_test)
print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1]))
120/120 [==============================] - 5s 46ms/step
Loss = 0.13334427624940873
Test Accuracy = 0.9333333373069763
For the purpose of this assignment, we’ve asked you to train the model only for two epochs. You can see that it achieves poor performances. Please go ahead and submit your assignment; to check correctness, the online grader will run your code only for a small number of epochs as well.
Example of the trained model (NOT PROVIDED)¶
After you have finished this official (graded) part of this assignment, you can also optionally train the ResNet for more iterations, if you want. We get a lot better performance when we train for ~20 epochs, but this will take more than an hour when training on a CPU.
Using a GPU, we’ve trained our own ResNet50 model’s weights on the SIGNS dataset. You can load and run our trained model on the test set in the cells below. It may take ≈1min to load the model.
model = load_model('ResNet50.h5')
preds = model.evaluate(X_test, Y_test)
print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1]))
注：因为未提供，所以博主没有做这个，它的意思是让你使用他用GPU训练好的模型在测试集上做评估。
ResNet50 is a powerful model for image classification when it is trained for an adequate number of iterations. We hope you can use what you’ve learnt and apply it to your own classification problem to perform state-of-the-art accuracy.
Congratulations on finishing this assignment! You’ve now implemented a state-of-the-art image classification system!
4 - Test on your own image (Optional/Ungraded)
If you wish, you can also take a picture of your own hand and see the output of the model. To do this:
1. Click on “File” in the upper bar of this notebook, then click “Open” to go on your Coursera Hub.
2. Add your image to this Jupyter Notebook’s directory, in the “images” folder
3. Write your image’s name in the following code
4. Run the code and check if the algorithm is right!
img_path = 'images/my_image.jpg'
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)
my_image = scipy.misc.imread(img_path)
imshow(my_image)
print("class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = ")
print(model.predict(x))
Input image shape: (1, 64, 64, 3)
C:UserswanghAnaconda3envs ensorflowlibsite-packagesipykernel_launcher.py:7: DeprecationWarning: `imread` is deprecated!
`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.
Use ``imageio.imread`` instead.
import sys
class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] =
[[1. 0. 0. 0. 0. 0.]]
这恰好是个反例==。
You can also print a summary of your model by running the following code.
model.summary()
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 64, 64, 3) 0
__________________________________________________________________________________________________
zero_padding2d_1 (ZeroPadding2D (None, 70, 70, 3) 0 input_1[0][0]
__________________________________________________________________________________________________
conv (Conv2D) (None, 32, 32, 64) 9472 zero_padding2d_1[0][0]
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization) (None, 32, 32, 64) 256 conv[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 32, 32, 64) 0 bn_conv1[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 15, 15, 64) 0 activation_4[0][0]
__________________________________________________________________________________________________
res2a_branch2a (Conv2D) (None, 15, 15, 64) 4160 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
bn2a_branch2a (BatchNormalizati (None, 15, 15, 64) 256 res2a_branch2a[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, 15, 15, 64) 0 bn2a_branch2a[0][0]
__________________________________________________________________________________________________
res2a_branch2b (Conv2D) (None, 15, 15, 64) 36928 activation_5[0][0]
__________________________________________________________________________________________________
bn2a_branch2b (BatchNormalizati (None, 15, 15, 64) 256 res2a_branch2b[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, 15, 15, 64) 0 bn2a_branch2b[0][0]
__________________________________________________________________________________________________
res2a_branch2c (Conv2D) (None, 15, 15, 256) 16640 activation_6[0][0]
__________________________________________________________________________________________________
res2a_branch1 (Conv2D) (None, 15, 15, 256) 16640 max_pooling2d_1[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_2 (Add) (None, 15, 15, 256) 0 bn2a_branch2c[0][0]
bn2a_branch1[0][0]
__________________________________________________________________________________________________
activation_7 (Activation) (None, 15, 15, 256) 0 add_2[0][0]
__________________________________________________________________________________________________
res2b_branch2a (Conv2D) (None, 15, 15, 64) 16448 activation_7[0][0]
__________________________________________________________________________________________________
bn2b_branch2a (BatchNormalizati (None, 15, 15, 64) 256 res2b_branch2a[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, 15, 15, 64) 0 bn2b_branch2a[0][0]
__________________________________________________________________________________________________
res2b_branch2b (Conv2D) (None, 15, 15, 64) 36928 activation_8[0][0]
__________________________________________________________________________________________________
bn2b_branch2b (BatchNormalizati (None, 15, 15, 64) 256 res2b_branch2b[0][0]
__________________________________________________________________________________________________
activation_9 (Activation) (None, 15, 15, 64) 0 bn2b_branch2b[0][0]
__________________________________________________________________________________________________
res2b_branch2c (Conv2D) (None, 15, 15, 256) 16640 activation_9[0][0]
__________________________________________________________________________________________________
bn2b_branch2c (BatchNormalizati (None, 15, 15, 256) 1024 res2b_branch2c[0][0]
__________________________________________________________________________________________________
add_3 (Add) (None, 15, 15, 256) 0 bn2b_branch2c[0][0]
activation_7[0][0]
__________________________________________________________________________________________________
activation_10 (Activation) (None, 15, 15, 256) 0 add_3[0][0]
__________________________________________________________________________________________________
res2c_branch2a (Conv2D) (None, 15, 15, 64) 16448 activation_10[0][0]
__________________________________________________________________________________________________
bn2c_branch2a (BatchNormalizati (None, 15, 15, 64) 256 res2c_branch2a[0][0]
__________________________________________________________________________________________________
activation_11 (Activation) (None, 15, 15, 64) 0 bn2c_branch2a[0][0]
__________________________________________________________________________________________________
res2c_branch2b (Conv2D) (None, 15, 15, 64) 36928 activation_11[0][0]
__________________________________________________________________________________________________
bn2c_branch2b (BatchNormalizati (None, 15, 15, 64) 256 res2c_branch2b[0][0]
__________________________________________________________________________________________________
activation_12 (Activation) (None, 15, 15, 64) 0 bn2c_branch2b[0][0]
__________________________________________________________________________________________________
res2c_branch2c (Conv2D) (None, 15, 15, 256) 16640 activation_12[0][0]
__________________________________________________________________________________________________
bn2c_branch2c (BatchNormalizati (None, 15, 15, 256) 1024 res2c_branch2c[0][0]
__________________________________________________________________________________________________
add_4 (Add) (None, 15, 15, 256) 0 bn2c_branch2c[0][0]
activation_10[0][0]
__________________________________________________________________________________________________
activation_13 (Activation) (None, 15, 15, 256) 0 add_4[0][0]
__________________________________________________________________________________________________
res3a_branch2a (Conv2D) (None, 15, 15, 128) 32896 activation_13[0][0]
__________________________________________________________________________________________________
bn3a_branch2a (BatchNormalizati (None, 15, 15, 128) 512 res3a_branch2a[0][0]
__________________________________________________________________________________________________
activation_14 (Activation) (None, 15, 15, 128) 0 bn3a_branch2a[0][0]
__________________________________________________________________________________________________
res3a_branch2b (Conv2D) (None, 15, 15, 128) 147584 activation_14[0][0]
__________________________________________________________________________________________________
bn3a_branch2b (BatchNormalizati (None, 15, 15, 128) 512 res3a_branch2b[0][0]
__________________________________________________________________________________________________
activation_15 (Activation) (None, 15, 15, 128) 0 bn3a_branch2b[0][0]
__________________________________________________________________________________________________
res3a_branch2c (Conv2D) (None, 15, 15, 512) 66048 activation_15[0][0]
__________________________________________________________________________________________________
res3a_branch1 (Conv2D) (None, 15, 15, 512) 131584 activation_13[0][0]
__________________________________________________________________________________________________
bn3a_branch2c (BatchNormalizati (None, 15, 15, 512) 2048 res3a_branch2c[0][0]
__________________________________________________________________________________________________
bn3a_branch1 (BatchNormalizatio (None, 15, 15, 512) 2048 res3a_branch1[0][0]
__________________________________________________________________________________________________
add_5 (Add) (None, 15, 15, 512) 0 bn3a_branch2c[0][0]
bn3a_branch1[0][0]
__________________________________________________________________________________________________
activation_16 (Activation) (None, 15, 15, 512) 0 add_5[0][0]
__________________________________________________________________________________________________
res3b_branch2a (Conv2D) (None, 15, 15, 128) 65664 activation_16[0][0]
__________________________________________________________________________________________________
bn3b_branch2a (BatchNormalizati (None, 15, 15, 128) 512 res3b_branch2a[0][0]
__________________________________________________________________________________________________
activation_17 (Activation) (None, 15, 15, 128) 0 bn3b_branch2a[0][0]
__________________________________________________________________________________________________
res3b_branch2b (Conv2D) (None, 15, 15, 128) 147584 activation_17[0][0]
__________________________________________________________________________________________________
bn3b_branch2b (BatchNormalizati (None, 15, 15, 128) 512 res3b_branch2b[0][0]
__________________________________________________________________________________________________
activation_18 (Activation) (None, 15, 15, 128) 0 bn3b_branch2b[0][0]
__________________________________________________________________________________________________
res3b_branch2c (Conv2D) (None, 15, 15, 512) 66048 activation_18[0][0]
__________________________________________________________________________________________________
bn3b_branch2c (BatchNormalizati (None, 15, 15, 512) 2048 res3b_branch2c[0][0]
__________________________________________________________________________________________________
add_6 (Add) (None, 15, 15, 512) 0 bn3b_branch2c[0][0]
activation_16[0][0]
__________________________________________________________________________________________________
activation_19 (Activation) (None, 15, 15, 512) 0 add_6[0][0]
__________________________________________________________________________________________________
res3c_branch2a (Conv2D) (None, 15, 15, 128) 65664 activation_19[0][0]
__________________________________________________________________________________________________
bn3c_branch2a (BatchNormalizati (None, 15, 15, 128) 512 res3c_branch2a[0][0]
__________________________________________________________________________________________________
activation_20 (Activation) (None, 15, 15, 128) 0 bn3c_branch2a[0][0]
__________________________________________________________________________________________________
res3c_branch2b (Conv2D) (None, 15, 15, 128) 147584 activation_20[0][0]
__________________________________________________________________________________________________
bn3c_branch2b (BatchNormalizati (None, 15, 15, 128) 512 res3c_branch2b[0][0]
__________________________________________________________________________________________________
activation_21 (Activation) (None, 15, 15, 128) 0 bn3c_branch2b[0][0]
__________________________________________________________________________________________________
res3c_branch2c (Conv2D) (None, 15, 15, 512) 66048 activation_21[0][0]
__________________________________________________________________________________________________
bn3c_branch2c (BatchNormalizati (None, 15, 15, 512) 2048 res3c_branch2c[0][0]
__________________________________________________________________________________________________
add_7 (Add) (None, 15, 15, 512) 0 bn3c_branch2c[0][0]
activation_19[0][0]
__________________________________________________________________________________________________
activation_22 (Activation) (None, 15, 15, 512) 0 add_7[0][0]
__________________________________________________________________________________________________
res3d_branch2a (Conv2D) (None, 15, 15, 128) 65664 activation_22[0][0]
__________________________________________________________________________________________________
bn3d_branch2a (BatchNormalizati (None, 15, 15, 128) 512 res3d_branch2a[0][0]
__________________________________________________________________________________________________
activation_23 (Activation) (None, 15, 15, 128) 0 bn3d_branch2a[0][0]
__________________________________________________________________________________________________
res3d_branch2b (Conv2D) (None, 15, 15, 128) 147584 activation_23[0][0]
__________________________________________________________________________________________________
bn3d_branch2b (BatchNormalizati (None, 15, 15, 128) 512 res3d_branch2b[0][0]
__________________________________________________________________________________________________
activation_24 (Activation) (None, 15, 15, 128) 0 bn3d_branch2b[0][0]
__________________________________________________________________________________________________
res3d_branch2c (Conv2D) (None, 15, 15, 512) 66048 activation_24[0][0]
__________________________________________________________________________________________________
bn3d_branch2c (BatchNormalizati (None, 15, 15, 512) 2048 res3d_branch2c[0][0]
__________________________________________________________________________________________________
add_8 (Add) (None, 15, 15, 512) 0 bn3d_branch2c[0][0]
activation_22[0][0]
__________________________________________________________________________________________________
activation_25 (Activation) (None, 15, 15, 512) 0 add_8[0][0]
__________________________________________________________________________________________________
res4a_branch2a (Conv2D) (None, 8, 8, 256) 131328 activation_25[0][0]
__________________________________________________________________________________________________
bn4a_branch2a (BatchNormalizati (None, 8, 8, 256) 1024 res4a_branch2a[0][0]
__________________________________________________________________________________________________
activation_26 (Activation) (None, 8, 8, 256) 0 bn4a_branch2a[0][0]
__________________________________________________________________________________________________
res4a_branch2b (Conv2D) (None, 8, 8, 256) 590080 activation_26[0][0]
__________________________________________________________________________________________________
bn4a_branch2b (BatchNormalizati (None, 8, 8, 256) 1024 res4a_branch2b[0][0]
__________________________________________________________________________________________________
activation_27 (Activation) (None, 8, 8, 256) 0 bn4a_branch2b[0][0]
__________________________________________________________________________________________________
res4a_branch2c (Conv2D) (None, 8, 8, 1024) 263168 activation_27[0][0]
__________________________________________________________________________________________________
res4a_branch1 (Conv2D) (None, 8, 8, 1024) 525312 activation_25[0][0]
__________________________________________________________________________________________________
bn4a_branch2c (BatchNormalizati (None, 8, 8, 1024) 4096 res4a_branch2c[0][0]
__________________________________________________________________________________________________
bn4a_branch1 (BatchNormalizatio (None, 8, 8, 1024) 4096 res4a_branch1[0][0]
__________________________________________________________________________________________________
add_9 (Add) (None, 8, 8, 1024) 0 bn4a_branch2c[0][0]
bn4a_branch1[0][0]
__________________________________________________________________________________________________
activation_28 (Activation) (None, 8, 8, 1024) 0 add_9[0][0]
__________________________________________________________________________________________________
res4b_branch2a (Conv2D) (None, 8, 8, 256) 262400 activation_28[0][0]
__________________________________________________________________________________________________
bn4b_branch2a (BatchNormalizati (None, 8, 8, 256) 1024 res4b_branch2a[0][0]
__________________________________________________________________________________________________
activation_29 (Activation) (None, 8, 8, 256) 0 bn4b_branch2a[0][0]
__________________________________________________________________________________________________
res4b_branch2b (Conv2D) (None, 8, 8, 256) 590080 activation_29[0][0]
__________________________________________________________________________________________________
bn4b_branch2b (BatchNormalizati (None, 8, 8, 256) 1024 res4b_branch2b[0][0]
__________________________________________________________________________________________________
activation_30 (Activation) (None, 8, 8, 256) 0 bn4b_branch2b[0][0]
__________________________________________________________________________________________________
res4b_branch2c (Conv2D) (None, 8, 8, 1024) 263168 activation_30[0][0]
__________________________________________________________________________________________________
bn4b_branch2c (BatchNormalizati (None, 8, 8, 1024) 4096 res4b_branch2c[0][0]
__________________________________________________________________________________________________
add_10 (Add) (None, 8, 8, 1024) 0 bn4b_branch2c[0][0]
activation_28[0][0]
__________________________________________________________________________________________________
activation_31 (Activation) (None, 8, 8, 1024) 0 add_10[0][0]
__________________________________________________________________________________________________
res4c_branch2a (Conv2D) (None, 8, 8, 256) 262400 activation_31[0][0]
__________________________________________________________________________________________________
bn4c_branch2a (BatchNormalizati (None, 8, 8, 256) 1024 res4c_branch2a[0][0]
__________________________________________________________________________________________________
activation_32 (Activation) (None, 8, 8, 256) 0 bn4c_branch2a[0][0]
__________________________________________________________________________________________________
res4c_branch2b (Conv2D) (None, 8, 8, 256) 590080 activation_32[0][0]
__________________________________________________________________________________________________
bn4c_branch2b (BatchNormalizati (None, 8, 8, 256) 1024 res4c_branch2b[0][0]
__________________________________________________________________________________________________
activation_33 (Activation) (None, 8, 8, 256) 0 bn4c_branch2b[0][0]
__________________________________________________________________________________________________
res4c_branch2c (Conv2D) (None, 8, 8, 1024) 263168 activation_33[0][0]
__________________________________________________________________________________________________
bn4c_branch2c (BatchNormalizati (None, 8, 8, 1024) 4096 res4c_branch2c[0][0]
__________________________________________________________________________________________________
add_11 (Add) (None, 8, 8, 1024) 0 bn4c_branch2c[0][0]
activation_31[0][0]
__________________________________________________________________________________________________
activation_34 (Activation) (None, 8, 8, 1024) 0 add_11[0][0]
__________________________________________________________________________________________________
res4d_branch2a (Conv2D) (None, 8, 8, 256) 262400 activation_34[0][0]
__________________________________________________________________________________________________
bn4d_branch2a (BatchNormalizati (None, 8, 8, 256) 1024 res4d_branch2a[0][0]
__________________________________________________________________________________________________
activation_35 (Activation) (None, 8, 8, 256) 0 bn4d_branch2a[0][0]
__________________________________________________________________________________________________
res4d_branch2b (Conv2D) (None, 8, 8, 256) 590080 activation_35[0][0]
__________________________________________________________________________________________________
bn4d_branch2b (BatchNormalizati (None, 8, 8, 256) 1024 res4d_branch2b[0][0]
__________________________________________________________________________________________________
activation_36 (Activation) (None, 8, 8, 256) 0 bn4d_branch2b[0][0]
__________________________________________________________________________________________________
res4d_branch2c (Conv2D) (None, 8, 8, 1024) 263168 activation_36[0][0]
__________________________________________________________________________________________________
bn4d_branch2c (BatchNormalizati (None, 8, 8, 1024) 4096 res4d_branch2c[0][0]
__________________________________________________________________________________________________
add_12 (Add) (None, 8, 8, 1024) 0 bn4d_branch2c[0][0]
activation_34[0][0]
__________________________________________________________________________________________________
activation_37 (Activation) (None, 8, 8, 1024) 0 add_12[0][0]
__________________________________________________________________________________________________
res4e_branch2a (Conv2D) (None, 8, 8, 256) 262400 activation_37[0][0]
__________________________________________________________________________________________________
bn4e_branch2a (BatchNormalizati (None, 8, 8, 256) 1024 res4e_branch2a[0][0]
__________________________________________________________________________________________________
activation_38 (Activation) (None, 8, 8, 256) 0 bn4e_branch2a[0][0]
__________________________________________________________________________________________________
res4e_branch2b (Conv2D) (None, 8, 8, 256) 590080 activation_38[0][0]
__________________________________________________________________________________________________
bn4e_branch2b (BatchNormalizati (None, 8, 8, 256) 1024 res4e_branch2b[0][0]
__________________________________________________________________________________________________
activation_39 (Activation) (None, 8, 8, 256) 0 bn4e_branch2b[0][0]
__________________________________________________________________________________________________
res4e_branch2c (Conv2D) (None, 8, 8, 1024) 263168 activation_39[0][0]
_____________________________________________________________________________________
_____________
bn4e_branch2c (BatchNormalizati (None, 8, 8, 1024) 4096 res4e_branch2c[0][0]
__________________________________________________________________________________________________
add_13 (Add) (None, 8, 8, 1024) 0 bn4e_branch2c[0][0]
activation_37[0][0]
__________________________________________________________________________________________________
activation_40 (Activation) (None, 8, 8, 1024) 0 add_13[0][0]
__________________________________________________________________________________________________
res4f_branch2a (Conv2D) (None, 8, 8, 256) 262400 activation_40[0][0]
__________________________________________________________________________________________________
bn4f_branch2a (BatchNormalizati (None, 8, 8, 256) 1024 res4f_branch2a[0][0]
__________________________________________________________________________________________________
activation_41 (Activation) (None, 8, 8, 256) 0 bn4f_branch2a[0][0]
__________________________________________________________________________________________________
res4f_branch2b (Conv2D) (None, 8, 8, 256) 590080 activation_41[0][0]
__________________________________________________________________________________________________
bn4f_branch2b (BatchNormalizati (None, 8, 8, 256) 1024 res4f_branch2b[0][0]
__________________________________________________________________________________________________
activation_42 (Activation) (None, 8, 8, 256) 0 bn4f_branch2b[0][0]
__________________________________________________________________________________________________
res4f_branch2c (Conv2D) (None, 8, 8, 1024) 263168 activation_42[0][0]
__________________________________________________________________________________________________
bn4f_branch2c (BatchNormalizati (None, 8, 8, 1024) 4096 res4f_branch2c[0][0]
__________________________________________________________________________________________________
add_14 (Add) (None, 8, 8, 1024) 0 bn4f_branch2c[0][0]
activation_40[0][0]
__________________________________________________________________________________________________
activation_43 (Activation) (None, 8, 8, 1024) 0 add_14[0][0]
__________________________________________________________________________________________________
res5a_branch2a (Conv2D) (None, 4, 4, 512) 524800 activation_43[0][0]
__________________________________________________________________________________________________
bn5a_branch2a (BatchNormalizati (None, 4, 4, 512) 2048 res5a_branch2a[0][0]
__________________________________________________________________________________________________
activation_44 (Activation) (None, 4, 4, 512) 0 bn5a_branch2a[0][0]
__________________________________________________________________________________________________
res5a_branch2b (Conv2D) (None, 4, 4, 512) 2359808 activation_44[0][0]
__________________________________________________________________________________________________
bn5a_branch2b (BatchNormalizati (None, 4, 4, 512) 2048 res5a_branch2b[0][0]
__________________________________________________________________________________________________
activation_45 (Activation) (None, 4, 4, 512) 0 bn5a_branch2b[0][0]
__________________________________________________________________________________________________
res5a_branch2c (Conv2D) (None, 4, 4, 2048) 1050624 activation_45[0][0]
__________________________________________________________________________________________________
res5a_branch1 (Conv2D) (None, 4, 4, 2048) 2099200 activation_43[0][0]
__________________________________________________________________________________________________
bn5a_branch2c (BatchNormalizati (None, 4, 4, 2048) 8192 res5a_branch2c[0][0]
__________________________________________________________________________________________________
bn5a_branch1 (BatchNormalizatio (None, 4, 4, 2048) 8192 res5a_branch1[0][0]
__________________________________________________________________________________________________
add_15 (Add) (None, 4, 4, 2048) 0 bn5a_branch2c[0][0]
bn5a_branch1[0][0]
__________________________________________________________________________________________________
activation_46 (Activation) (None, 4, 4, 2048) 0 add_15[0][0]
__________________________________________________________________________________________________
res5b_branch2a (Conv2D) (None, 4, 4, 512) 1049088 activation_46[0][0]
__________________________________________________________________________________________________
bn5b_branch2a (BatchNormalizati (None, 4, 4, 512) 2048 res5b_branch2a[0][0]
__________________________________________________________________________________________________
activation_47 (Activation) (None, 4, 4, 512) 0 bn5b_branch2a[0][0]
__________________________________________________________________________________________________
res5b_branch2b (Conv2D) (None, 4, 4, 512) 2359808 activation_47[0][0]
__________________________________________________________________________________________________
bn5b_branch2b (BatchNormalizati (None, 4, 4, 512) 2048 res5b_branch2b[0][0]
__________________________________________________________________________________________________
activation_48 (Activation) (None, 4, 4, 512) 0 bn5b_branch2b[0][0]
__________________________________________________________________________________________________
res5b_branch2c (Conv2D) (None, 4, 4, 2048) 1050624 activation_48[0][0]
__________________________________________________________________________________________________
bn5b_branch2c (BatchNormalizati (None, 4, 4, 2048) 8192 res5b_branch2c[0][0]
__________________________________________________________________________________________________
add_16 (Add) (None, 4, 4, 2048) 0 bn5b_branch2c[0][0]
activation_46[0][0]
__________________________________________________________________________________________________
activation_49 (Activation) (None, 4, 4, 2048) 0 add_16[0][0]
__________________________________________________________________________________________________
res5c_branch2a (Conv2D) (None, 4, 4, 512) 1049088 activation_49[0][0]
__________________________________________________________________________________________________
bn5c_branch2a (BatchNormalizati (None, 4, 4, 512) 2048 res5c_branch2a[0][0]
__________________________________________________________________________________________________
activation_50 (Activation) (None, 4, 4, 512) 0 bn5c_branch2a[0][0]
__________________________________________________________________________________________________
res5c_branch2b (Conv2D) (None, 4, 4, 512) 2359808 activation_50[0][0]
__________________________________________________________________________________________________
bn5c_branch2b (BatchNormalizati (None, 4, 4, 512) 2048 res5c_branch2b[0][0]
__________________________________________________________________________________________________
activation_51 (Activation) (None, 4, 4, 512) 0 bn5c_branch2b[0][0]
__________________________________________________________________________________________________
res5c_branch2c (Conv2D) (None, 4, 4, 2048) 1050624 activation_51[0][0]
__________________________________________________________________________________________________
bn5c_branch2c (BatchNormalizati (None, 4, 4, 2048) 8192 res5c_branch2c[0][0]
__________________________________________________________________________________________________
add_17 (Add) (None, 4, 4, 2048) 0 bn5c_branch2c[0][0]
activation_49[0][0]
__________________________________________________________________________________________________
activation_52 (Activation) (None, 4, 4, 2048) 0 add_17[0][0]
__________________________________________________________________________________________________
average_pooling2d_1 (AveragePoo (None, 2, 2, 2048) 0 activation_52[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 8192) 0 average_pooling2d_1[0][0]
__________________________________________________________________________________________________
fc6 (Dense) (None, 6) 49158 flatten_1[0][0]
==================================================================================================
Total params: 23,636,870
Trainable params: 23,583,750
Non-trainable params: 53,120
__________________________________________________________________________________________________
Finally, run the code below to visualize your ResNet50. You can also download a .png picture of your model by going to “File -> Open…-> model.png”.
plot_model(model, to_file='model.png')
SVG(model_to_dot(model).create(prog='dot', format='svg'))
What you should remember:
- Very deep “plain” networks don’t work in practice because they are hard to train due to vanishing gradients.
- The skip-connections help to address the Vanishing Gradient problem. They also make it easy for a ResNet block to learn an identity function.
- There are two main type of blocks: The identity block and the convolutional block.
- Very deep Residual Networks are built by stacking these blocks together.
References
This notebook presents the ResNet algorithm due to He et al. (2015). The implementation here also took significant inspiration and follows the structure given in the github repository of Francois Chollet:
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun - Deep Residual Learning for Image Recognition (2015)
- Francois Chollet’s github repository: https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py