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Residual Networks

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此压缩包里没有 Coursera 训练好的 ResNet-50 网络的权重

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:

Updates

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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
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.

Figure 1 : Vanishing gradient The speed of learning decreases very rapidly for the shallower 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 model to skip layers:

Figure 2 : A ResNet block showing a skip-connection

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 accounts for ResNets' remarkable performance even more so than skip connections helping with vanishing gradients).

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: the "identity block" and the "convolutional block."

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 \(a^{[l]}\)) has the same dimension as the output activation (say \(a^{[l+2]}\)). To flesh out the different steps of what happens in a ResNet's identity block, here is an alternative diagram showing the individual steps:

Figure 3 : Identity block. Skip connection "skips over" 2 layers.

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:

Figure 4 : Identity block. Skip connection "skips over" 3 layers.

Here are the individual steps.

First component of main path:

Second component of main path:

Third component of main path:

Final step:

Exercise: Implement the ResNet identity block. We have implemented the first component of the main path. Please read this carefully to make sure you understand what it is doing. You should implement the rest.

# 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)
    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)
    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)

    # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)
    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 = [ 0.94822985  0.          1.16101444  2.747859    0.          1.36677003]

2.2 - The convolutional block

The ResNet "convolutional block" is the second block type. 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:

Figure 4 : Convolutional block

The details of the convolutional block are as follows.

First component of main path:

Second component of main path:

Third component of main path:

Shortcut path:

Final step:

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.

# 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', 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(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)
    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)

    ##### SHORTCUT PATH #### (≈2 lines)
    X_shortcut = Conv2D(filters=F3, kernel_size=(1, 1), strides=(s, s), padding='valid', name=conv_name_base+'1', 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 = 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  1.23489773  0.46822017  0.0367176   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.

Figure 5 : ResNet-50 model

The details of this ResNet-50 model are:

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:

Here are some other functions we used in the code below:

# 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(64, (7, 7), strides = (2, 2), name = 'conv1', kernel_initializer = glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis = 3, name = 'bn_conv1')(X)
    X = Activation('relu')(X)
    X = MaxPooling2D((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, 3, [64, 64, 256], stage=2, block='b')
    X = identity_block(X, 3, [64, 64, 256], stage=2, block='c')

    ### START CODE HERE ###

    # Stage 3 (≈4 lines)
    X = convolutional_block(X, f=3, filters=[128, 128, 512], stage=3, block='a')
    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)
    X = convolutional_block(X, f=3, filters=[256, 256, 1024], stage=4, block='a')
    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')

    # Stage 5 (≈3 lines)
    X = convolutional_block(X, f=3, filters=[512, 512, 2048], stage=5, block='a')
    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')

    # AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)"
    X = AveragePooling2D(pool_size=(2, 2), name='avg_pool')(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.

Figure 6 : 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.

# 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 = 2, batch_size = 32)
Epoch 1/2
1080/1080 [==============================] - 262s - loss: 2.3049 - acc: 0.4037   
Epoch 2/2
1080/1080 [==============================] - 258s - loss: 1.2344 - acc: 0.6815   

<keras.callbacks.History at 0x7f20ad041710>

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 [==============================] - 9s     
Loss = 1.99955883026
Test Accuracy = 0.166666668653

For the purpose of this assignment, we've asked you to train the model for just 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.

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]))
120/120 [==============================] - 9s     
Loss = 0.530178320408
Test Accuracy = 0.866666662693

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 = x/255.0
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)
class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = 
[[  3.41876671e-06   2.77412561e-04   9.99522924e-01   1.98842812e-07
    1.95619068e-04   4.11686671e-07]]

output_36_1

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]                    
____________________________________________________________________________________________________
conv1 (Conv2D)                   (None, 32, 32, 64)    9472        zero_padding2d_1[0][0]           
____________________________________________________________________________________________________
bn_conv1 (BatchNormalization)    (None, 32, 32, 64)    256         conv1[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 (BatchNormalizatio (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 (BatchNormalizatio (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 (BatchNormalizatio (None, 15, 15, 256)   1024        res2a_branch2c[0][0]             
____________________________________________________________________________________________________
bn2a_branch1 (BatchNormalization (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 (BatchNormalizatio (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 (BatchNormalizatio (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 (BatchNormalizatio (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 (BatchNormalizatio (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 (BatchNormalizatio (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 (BatchNormalizatio (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, 8, 8, 128)     32896       activation_13[0][0]              
____________________________________________________________________________________________________
bn3a_branch2a (BatchNormalizatio (None, 8, 8, 128)     512         res3a_branch2a[0][0]             
____________________________________________________________________________________________________
activation_14 (Activation)       (None, 8, 8, 128)     0           bn3a_branch2a[0][0]              
____________________________________________________________________________________________________
res3a_branch2b (Conv2D)          (None, 8, 8, 128)     147584      activation_14[0][0]              
____________________________________________________________________________________________________
bn3a_branch2b (BatchNormalizatio (None, 8, 8, 128)     512         res3a_branch2b[0][0]             
____________________________________________________________________________________________________
activation_15 (Activation)       (None, 8, 8, 128)     0           bn3a_branch2b[0][0]              
____________________________________________________________________________________________________
res3a_branch2c (Conv2D)          (None, 8, 8, 512)     66048       activation_15[0][0]              
____________________________________________________________________________________________________
res3a_branch1 (Conv2D)           (None, 8, 8, 512)     131584      activation_13[0][0]              
____________________________________________________________________________________________________
bn3a_branch2c (BatchNormalizatio (None, 8, 8, 512)     2048        res3a_branch2c[0][0]             
____________________________________________________________________________________________________
bn3a_branch1 (BatchNormalization (None, 8, 8, 512)     2048        res3a_branch1[0][0]              
____________________________________________________________________________________________________
add_5 (Add)                      (None, 8, 8, 512)     0           bn3a_branch2c[0][0]              
                                                                   bn3a_branch1[0][0]               
____________________________________________________________________________________________________
activation_16 (Activation)       (None, 8, 8, 512)     0           add_5[0][0]                      
____________________________________________________________________________________________________
res3b_branch2a (Conv2D)          (None, 8, 8, 128)     65664       activation_16[0][0]              
____________________________________________________________________________________________________
bn3b_branch2a (BatchNormalizatio (None, 8, 8, 128)     512         res3b_branch2a[0][0]             
____________________________________________________________________________________________________
activation_17 (Activation)       (None, 8, 8, 128)     0           bn3b_branch2a[0][0]              
____________________________________________________________________________________________________
res3b_branch2b (Conv2D)          (None, 8, 8, 128)     147584      activation_17[0][0]              
____________________________________________________________________________________________________
bn3b_branch2b (BatchNormalizatio (None, 8, 8, 128)     512         res3b_branch2b[0][0]             
____________________________________________________________________________________________________
activation_18 (Activation)       (None, 8, 8, 128)     0           bn3b_branch2b[0][0]              
____________________________________________________________________________________________________
res3b_branch2c (Conv2D)          (None, 8, 8, 512)     66048       activation_18[0][0]              
____________________________________________________________________________________________________
bn3b_branch2c (BatchNormalizatio (None, 8, 8, 512)     2048        res3b_branch2c[0][0]             
____________________________________________________________________________________________________
add_6 (Add)                      (None, 8, 8, 512)     0           bn3b_branch2c[0][0]              
                                                                   activation_16[0][0]              
____________________________________________________________________________________________________
activation_19 (Activation)       (None, 8, 8, 512)     0           add_6[0][0]                      
____________________________________________________________________________________________________
res3c_branch2a (Conv2D)          (None, 8, 8, 128)     65664       activation_19[0][0]              
____________________________________________________________________________________________________
bn3c_branch2a (BatchNormalizatio (None, 8, 8, 128)     512         res3c_branch2a[0][0]             
____________________________________________________________________________________________________
activation_20 (Activation)       (None, 8, 8, 128)     0           bn3c_branch2a[0][0]              
____________________________________________________________________________________________________
res3c_branch2b (Conv2D)          (None, 8, 8, 128)     147584      activation_20[0][0]              
____________________________________________________________________________________________________
bn3c_branch2b (BatchNormalizatio (None, 8, 8, 128)     512         res3c_branch2b[0][0]             
____________________________________________________________________________________________________
activation_21 (Activation)       (None, 8, 8, 128)     0           bn3c_branch2b[0][0]              
____________________________________________________________________________________________________
res3c_branch2c (Conv2D)          (None, 8, 8, 512)     66048       activation_21[0][0]              
____________________________________________________________________________________________________
bn3c_branch2c (BatchNormalizatio (None, 8, 8, 512)     2048        res3c_branch2c[0][0]             
____________________________________________________________________________________________________
add_7 (Add)                      (None, 8, 8, 512)     0           bn3c_branch2c[0][0]              
                                                                   activation_19[0][0]              
____________________________________________________________________________________________________
activation_22 (Activation)       (None, 8, 8, 512)     0           add_7[0][0]                      
____________________________________________________________________________________________________
res3d_branch2a (Conv2D)          (None, 8, 8, 128)     65664       activation_22[0][0]              
____________________________________________________________________________________________________
bn3d_branch2a (BatchNormalizatio (None, 8, 8, 128)     512         res3d_branch2a[0][0]             
____________________________________________________________________________________________________
activation_23 (Activation)       (None, 8, 8, 128)     0           bn3d_branch2a[0][0]              
____________________________________________________________________________________________________
res3d_branch2b (Conv2D)          (None, 8, 8, 128)     147584      activation_23[0][0]              
____________________________________________________________________________________________________
bn3d_branch2b (BatchNormalizatio (None, 8, 8, 128)     512         res3d_branch2b[0][0]             
____________________________________________________________________________________________________
activation_24 (Activation)       (None, 8, 8, 128)     0           bn3d_branch2b[0][0]              
____________________________________________________________________________________________________
res3d_branch2c (Conv2D)          (None, 8, 8, 512)     66048       activation_24[0][0]              
____________________________________________________________________________________________________
bn3d_branch2c (BatchNormalizatio (None, 8, 8, 512)     2048        res3d_branch2c[0][0]             
____________________________________________________________________________________________________
add_8 (Add)                      (None, 8, 8, 512)     0           bn3d_branch2c[0][0]              
                                                                   activation_22[0][0]              
____________________________________________________________________________________________________
activation_25 (Activation)       (None, 8, 8, 512)     0           add_8[0][0]                      
____________________________________________________________________________________________________
res4a_branch2a (Conv2D)          (None, 4, 4, 256)     131328      activation_25[0][0]              
____________________________________________________________________________________________________
bn4a_branch2a (BatchNormalizatio (None, 4, 4, 256)     1024        res4a_branch2a[0][0]             
____________________________________________________________________________________________________
activation_26 (Activation)       (None, 4, 4, 256)     0           bn4a_branch2a[0][0]              
____________________________________________________________________________________________________
res4a_branch2b (Conv2D)          (None, 4, 4, 256)     590080      activation_26[0][0]              
____________________________________________________________________________________________________
bn4a_branch2b (BatchNormalizatio (None, 4, 4, 256)     1024        res4a_branch2b[0][0]             
____________________________________________________________________________________________________
activation_27 (Activation)       (None, 4, 4, 256)     0           bn4a_branch2b[0][0]              
____________________________________________________________________________________________________
res4a_branch2c (Conv2D)          (None, 4, 4, 1024)    263168      activation_27[0][0]              
____________________________________________________________________________________________________
res4a_branch1 (Conv2D)           (None, 4, 4, 1024)    525312      activation_25[0][0]              
____________________________________________________________________________________________________
bn4a_branch2c (BatchNormalizatio (None, 4, 4, 1024)    4096        res4a_branch2c[0][0]             
____________________________________________________________________________________________________
bn4a_branch1 (BatchNormalization (None, 4, 4, 1024)    4096        res4a_branch1[0][0]              
____________________________________________________________________________________________________
add_9 (Add)                      (None, 4, 4, 1024)    0           bn4a_branch2c[0][0]              
                                                                   bn4a_branch1[0][0]               
____________________________________________________________________________________________________
activation_28 (Activation)       (None, 4, 4, 1024)    0           add_9[0][0]                      
____________________________________________________________________________________________________
res4b_branch2a (Conv2D)          (None, 4, 4, 256)     262400      activation_28[0][0]              
____________________________________________________________________________________________________
bn4b_branch2a (BatchNormalizatio (None, 4, 4, 256)     1024        res4b_branch2a[0][0]             
____________________________________________________________________________________________________
activation_29 (Activation)       (None, 4, 4, 256)     0           bn4b_branch2a[0][0]              
____________________________________________________________________________________________________
res4b_branch2b (Conv2D)          (None, 4, 4, 256)     590080      activation_29[0][0]              
____________________________________________________________________________________________________
bn4b_branch2b (BatchNormalizatio (None, 4, 4, 256)     1024        res4b_branch2b[0][0]             
____________________________________________________________________________________________________
activation_30 (Activation)       (None, 4, 4, 256)     0           bn4b_branch2b[0][0]              
____________________________________________________________________________________________________
res4b_branch2c (Conv2D)          (None, 4, 4, 1024)    263168      activation_30[0][0]              
____________________________________________________________________________________________________
bn4b_branch2c (BatchNormalizatio (None, 4, 4, 1024)    4096        res4b_branch2c[0][0]             
____________________________________________________________________________________________________
add_10 (Add)                     (None, 4, 4, 1024)    0           bn4b_branch2c[0][0]              
                                                                   activation_28[0][0]              
____________________________________________________________________________________________________
activation_31 (Activation)       (None, 4, 4, 1024)    0           add_10[0][0]                     
____________________________________________________________________________________________________
res4c_branch2a (Conv2D)          (None, 4, 4, 256)     262400      activation_31[0][0]              
____________________________________________________________________________________________________
bn4c_branch2a (BatchNormalizatio (None, 4, 4, 256)     1024        res4c_branch2a[0][0]             
____________________________________________________________________________________________________
activation_32 (Activation)       (None, 4, 4, 256)     0           bn4c_branch2a[0][0]              
____________________________________________________________________________________________________
res4c_branch2b (Conv2D)          (None, 4, 4, 256)     590080      activation_32[0][0]              
____________________________________________________________________________________________________
bn4c_branch2b (BatchNormalizatio (None, 4, 4, 256)     1024        res4c_branch2b[0][0]             
____________________________________________________________________________________________________
activation_33 (Activation)       (None, 4, 4, 256)     0           bn4c_branch2b[0][0]              
____________________________________________________________________________________________________
res4c_branch2c (Conv2D)          (None, 4, 4, 1024)    263168      activation_33[0][0]              
____________________________________________________________________________________________________
bn4c_branch2c (BatchNormalizatio (None, 4, 4, 1024)    4096        res4c_branch2c[0][0]             
____________________________________________________________________________________________________
add_11 (Add)                     (None, 4, 4, 1024)    0           bn4c_branch2c[0][0]              
                                                                   activation_31[0][0]              
____________________________________________________________________________________________________
activation_34 (Activation)       (None, 4, 4, 1024)    0           add_11[0][0]                     
____________________________________________________________________________________________________
res4d_branch2a (Conv2D)          (None, 4, 4, 256)     262400      activation_34[0][0]              
____________________________________________________________________________________________________
bn4d_branch2a (BatchNormalizatio (None, 4, 4, 256)     1024        res4d_branch2a[0][0]             
____________________________________________________________________________________________________
activation_35 (Activation)       (None, 4, 4, 256)     0           bn4d_branch2a[0][0]              
____________________________________________________________________________________________________
res4d_branch2b (Conv2D)          (None, 4, 4, 256)     590080      activation_35[0][0]              
____________________________________________________________________________________________________
bn4d_branch2b (BatchNormalizatio (None, 4, 4, 256)     1024        res4d_branch2b[0][0]             
____________________________________________________________________________________________________
activation_36 (Activation)       (None, 4, 4, 256)     0           bn4d_branch2b[0][0]              
____________________________________________________________________________________________________
res4d_branch2c (Conv2D)          (None, 4, 4, 1024)    263168      activation_36[0][0]              
____________________________________________________________________________________________________
bn4d_branch2c (BatchNormalizatio (None, 4, 4, 1024)    4096        res4d_branch2c[0][0]             
____________________________________________________________________________________________________
add_12 (Add)                     (None, 4, 4, 1024)    0           bn4d_branch2c[0][0]              
                                                                   activation_34[0][0]              
____________________________________________________________________________________________________
activation_37 (Activation)       (None, 4, 4, 1024)    0           add_12[0][0]                     
____________________________________________________________________________________________________
res4e_branch2a (Conv2D)          (None, 4, 4, 256)     262400      activation_37[0][0]              
____________________________________________________________________________________________________
bn4e_branch2a (BatchNormalizatio (None, 4, 4, 256)     1024        res4e_branch2a[0][0]             
____________________________________________________________________________________________________
activation_38 (Activation)       (None, 4, 4, 256)     0           bn4e_branch2a[0][0]              
____________________________________________________________________________________________________
res4e_branch2b (Conv2D)          (None, 4, 4, 256)     590080      activation_38[0][0]              
____________________________________________________________________________________________________
bn4e_branch2b (BatchNormalizatio (None, 4, 4, 256)     1024        res4e_branch2b[0][0]             
____________________________________________________________________________________________________
activation_39 (Activation)       (None, 4, 4, 256)     0           bn4e_branch2b[0][0]              
____________________________________________________________________________________________________
res4e_branch2c (Conv2D)          (None, 4, 4, 1024)    263168      activation_39[0][0]              
____________________________________________________________________________________________________
bn4e_branch2c (BatchNormalizatio (None, 4, 4, 1024)    4096        res4e_branch2c[0][0]             
____________________________________________________________________________________________________
add_13 (Add)                     (None, 4, 4, 1024)    0           bn4e_branch2c[0][0]              
                                                                   activation_37[0][0]              
____________________________________________________________________________________________________
activation_40 (Activation)       (None, 4, 4, 1024)    0           add_13[0][0]                     
____________________________________________________________________________________________________
res4f_branch2a (Conv2D)          (None, 4, 4, 256)     262400      activation_40[0][0]              
____________________________________________________________________________________________________
bn4f_branch2a (BatchNormalizatio (None, 4, 4, 256)     1024        res4f_branch2a[0][0]             
____________________________________________________________________________________________________
activation_41 (Activation)       (None, 4, 4, 256)     0           bn4f_branch2a[0][0]              
____________________________________________________________________________________________________
res4f_branch2b (Conv2D)          (None, 4, 4, 256)     590080      activation_41[0][0]              
____________________________________________________________________________________________________
bn4f_branch2b (BatchNormalizatio (None, 4, 4, 256)     1024        res4f_branch2b[0][0]             
____________________________________________________________________________________________________
activation_42 (Activation)       (None, 4, 4, 256)     0           bn4f_branch2b[0][0]              
____________________________________________________________________________________________________
res4f_branch2c (Conv2D)          (None, 4, 4, 1024)    263168      activation_42[0][0]              
____________________________________________________________________________________________________
bn4f_branch2c (BatchNormalizatio (None, 4, 4, 1024)    4096        res4f_branch2c[0][0]             
____________________________________________________________________________________________________
add_14 (Add)                     (None, 4, 4, 1024)    0           bn4f_branch2c[0][0]              
                                                                   activation_40[0][0]              
____________________________________________________________________________________________________
activation_43 (Activation)       (None, 4, 4, 1024)    0           add_14[0][0]                     
____________________________________________________________________________________________________
res5a_branch2a (Conv2D)          (None, 2, 2, 512)     524800      activation_43[0][0]              
____________________________________________________________________________________________________
bn5a_branch2a (BatchNormalizatio (None, 2, 2, 512)     2048        res5a_branch2a[0][0]             
____________________________________________________________________________________________________
activation_44 (Activation)       (None, 2, 2, 512)     0           bn5a_branch2a[0][0]              
____________________________________________________________________________________________________
res5a_branch2b (Conv2D)          (None, 2, 2, 512)     2359808     activation_44[0][0]              
____________________________________________________________________________________________________
bn5a_branch2b (BatchNormalizatio (None, 2, 2, 512)     2048        res5a_branch2b[0][0]             
____________________________________________________________________________________________________
activation_45 (Activation)       (None, 2, 2, 512)     0           bn5a_branch2b[0][0]              
____________________________________________________________________________________________________
res5a_branch2c (Conv2D)          (None, 2, 2, 2048)    1050624     activation_45[0][0]              
____________________________________________________________________________________________________
res5a_branch1 (Conv2D)           (None, 2, 2, 2048)    2099200     activation_43[0][0]              
____________________________________________________________________________________________________
bn5a_branch2c (BatchNormalizatio (None, 2, 2, 2048)    8192        res5a_branch2c[0][0]             
____________________________________________________________________________________________________
bn5a_branch1 (BatchNormalization (None, 2, 2, 2048)    8192        res5a_branch1[0][0]              
____________________________________________________________________________________________________
add_15 (Add)                     (None, 2, 2, 2048)    0           bn5a_branch2c[0][0]              
                                                                   bn5a_branch1[0][0]               
____________________________________________________________________________________________________
activation_46 (Activation)       (None, 2, 2, 2048)    0           add_15[0][0]                     
____________________________________________________________________________________________________
res5b_branch2a (Conv2D)          (None, 2, 2, 512)     1049088     activation_46[0][0]              
____________________________________________________________________________________________________
bn5b_branch2a (BatchNormalizatio (None, 2, 2, 512)     2048        res5b_branch2a[0][0]             
____________________________________________________________________________________________________
activation_47 (Activation)       (None, 2, 2, 512)     0           bn5b_branch2a[0][0]              
____________________________________________________________________________________________________
res5b_branch2b (Conv2D)          (None, 2, 2, 512)     2359808     activation_47[0][0]              
____________________________________________________________________________________________________
bn5b_branch2b (BatchNormalizatio (None, 2, 2, 512)     2048        res5b_branch2b[0][0]             
____________________________________________________________________________________________________
activation_48 (Activation)       (None, 2, 2, 512)     0           bn5b_branch2b[0][0]              
____________________________________________________________________________________________________
res5b_branch2c (Conv2D)          (None, 2, 2, 2048)    1050624     activation_48[0][0]              
____________________________________________________________________________________________________
bn5b_branch2c (BatchNormalizatio (None, 2, 2, 2048)    8192        res5b_branch2c[0][0]             
____________________________________________________________________________________________________
add_16 (Add)                     (None, 2, 2, 2048)    0           bn5b_branch2c[0][0]              
                                                                   activation_46[0][0]              
____________________________________________________________________________________________________
activation_49 (Activation)       (None, 2, 2, 2048)    0           add_16[0][0]                     
____________________________________________________________________________________________________
res5c_branch2a (Conv2D)          (None, 2, 2, 512)     1049088     activation_49[0][0]              
____________________________________________________________________________________________________
bn5c_branch2a (BatchNormalizatio (None, 2, 2, 512)     2048        res5c_branch2a[0][0]             
____________________________________________________________________________________________________
activation_50 (Activation)       (None, 2, 2, 512)     0           bn5c_branch2a[0][0]              
____________________________________________________________________________________________________
res5c_branch2b (Conv2D)          (None, 2, 2, 512)     2359808     activation_50[0][0]              
____________________________________________________________________________________________________
bn5c_branch2b (BatchNormalizatio (None, 2, 2, 512)     2048        res5c_branch2b[0][0]             
____________________________________________________________________________________________________
activation_51 (Activation)       (None, 2, 2, 512)     0           bn5c_branch2b[0][0]              
____________________________________________________________________________________________________
res5c_branch2c (Conv2D)          (None, 2, 2, 2048)    1050624     activation_51[0][0]              
____________________________________________________________________________________________________
bn5c_branch2c (BatchNormalizatio (None, 2, 2, 2048)    8192        res5c_branch2c[0][0]             
____________________________________________________________________________________________________
add_17 (Add)                     (None, 2, 2, 2048)    0           bn5c_branch2c[0][0]              
                                                                   activation_49[0][0]              
____________________________________________________________________________________________________
activation_52 (Activation)       (None, 2, 2, 2048)    0           add_17[0][0]                     
____________________________________________________________________________________________________
avg_pool (AveragePooling2D)      (None, 1, 1, 2048)    0           activation_52[0][0]              
____________________________________________________________________________________________________
flatten_1 (Flatten)              (None, 2048)          0           avg_pool[0][0]                   
____________________________________________________________________________________________________
fc6 (Dense)                      (None, 6)             12294       flatten_1[0][0]                  
====================================================================================================
Total params: 23,600,006
Trainable params: 23,546,886
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'))

image

What you should remember

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:

标签:None,name,activation,Residual,__________________________________________________
来源: https://www.cnblogs.com/geekfx/p/14220728.html