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.

In [1]:
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.

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:

**Figure 1** : **Vanishing gradient**
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:

**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--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]) 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're the individual steps.

First component of main path:

  • The first CONV2D has 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 F2 filters of shape (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 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
In [2]:
# GRADED FUNCTION: identity_block

def identity_block(X, f, filters, stage, block):
    """
    Implementation of the identity block as defined in Figure 3
    
    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
In [3]:
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]

Expected Output:

**out** [ 0.94822985 0. 1.16101444 2.747859 0. 1.36677003]

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:

**Figure 4** : **Convolutional block**

The CONV2D layer in the shortcut path is used to resize the input 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 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 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 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 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 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.

In [4]:
# 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


    # First component of main path
    X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (s,s), 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)
    

    ##### 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
In [5]:
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]

Expected Output:

**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:

  • 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 [512, 512, 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:

Here're some other functions we used in the code below:

In [6]:
# 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', s = 2)
    X = identity_block(X, 3, [128, 128, 512], stage=3, block='b')
    X = identity_block(X, 3, [128, 128, 512], stage=3, block='c')
    X = identity_block(X, 3, [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', s = 2)
    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='b')
    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='c')
    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='d')
    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='e')
    X = identity_block(X, 3, [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', s = 2)
    X = identity_block(X, 3, [512, 512, 2048], stage=5, block='b')
    X = identity_block(X, 3, [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
    #model1_out = Lambda(lambda x: K.round(x), output_shape=...)(X)
    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.

In [7]:
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.

In [8]:
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**
In [9]:
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.

In [10]:
model.fit(X_train, Y_train, epochs = 2, batch_size = 32)

Epoch 1/2
1080/1080 [==============================] - 294s - loss: 3.0532 - acc: 0.2639   
Epoch 2/2
1080/1080 [==============================] - 273s - loss: 2.0492 - acc: 0.3537
Out[10]:
<keras.callbacks.History at 0x7f07c1cf09e8>

Expected Output:

** Epoch 1/2** loss: between 1 and 5, acc: between 0.2 and 0.5, although your results can be different from ours.
** Epoch 2/2** loss: between 1 and 5, acc: between 0.2 and 0.5, you should see your loss decreasing and the accuracy increasing.

Let's see how this model (trained on only two epochs) performs on the test set.

In [11]:
preds = model.evaluate(X_test, Y_test)
print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1]))

120/120 [==============================] - 9s     
Loss = 2.22376348178
Test Accuracy = 0.166666666667

Expected Output:

**Test Accuracy** between 0.16 and 0.25

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.

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.

In [12]:
model = load_model('ResNet50.h5')
In [13]:
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!
In [14]:
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)
class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = 
[[ 1.  0.  0.  0.  0.  0.]]

You can also print a summary of your model by running the following code.

In [15]:
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".

In [16]:
plot_model(model, to_file='model.png')
SVG(model_to_dot(model).create(prog='dot', format='svg'))
Out[16]:
G 139670141307424 input_1: InputLayer 139670141307480 zero_padding2d_1: ZeroPadding2D 139670141307424->139670141307480 139670141307592 conv1: Conv2D 139670141307480->139670141307592 139670141307536 bn_conv1: BatchNormalization 139670141307592->139670141307536 139670141308376 activation_4: Activation 139670141307536->139670141308376 139670141349960 max_pooling2d_1: MaxPooling2D 139670141308376->139670141349960 139670141350128 res2a_branch2a: Conv2D 139670141349960->139670141350128 139670141351920 res2a_branch1: Conv2D 139670141349960->139670141351920 139670141350464 bn2a_branch2a: BatchNormalization 139670141350128->139670141350464 139670141350800 activation_5: Activation 139670141350464->139670141350800 139670141350856 res2a_branch2b: Conv2D 139670141350800->139670141350856 139670141351192 bn2a_branch2b: BatchNormalization 139670141350856->139670141351192 139670141351528 activation_6: Activation 139670141351192->139670141351528 139670141351584 res2a_branch2c: Conv2D 139670141351528->139670141351584 139670141352312 bn2a_branch2c: BatchNormalization 139670141351584->139670141352312 139670141352648 bn2a_branch1: BatchNormalization 139670141351920->139670141352648 139670141352928 add_2: Add 139670141352312->139670141352928 139670141352648->139670141352928 139670141352984 activation_7: Activation 139670141352928->139670141352984 139670141353040 res2b_branch2a: Conv2D 139670141352984->139670141353040 139671293625736 add_3: Add 139670141352984->139671293625736 139670141353376 bn2b_branch2a: BatchNormalization 139670141353040->139670141353376 139670141353712 activation_8: Activation 139670141353376->139670141353712 139670141353768 res2b_branch2b: Conv2D 139670141353712->139670141353768 139670141308880 bn2b_branch2b: BatchNormalization 139670141353768->139670141308880 139670141387272 activation_9: Activation 139670141308880->139670141387272 139670141387328 res2b_branch2c: Conv2D 139670141387272->139670141387328 139671293627976 bn2b_branch2c: BatchNormalization 139670141387328->139671293627976 139671293627976->139671293625736 139670141387832 activation_10: Activation 139671293625736->139670141387832 139670141387888 res2c_branch2a: Conv2D 139670141387832->139670141387888 139670141390016 add_4: Add 139670141387832->139670141390016 139670141388224 bn2c_branch2a: BatchNormalization 139670141387888->139670141388224 139670141388560 activation_11: Activation 139670141388224->139670141388560 139670141388616 res2c_branch2b: Conv2D 139670141388560->139670141388616 139670141388952 bn2c_branch2b: BatchNormalization 139670141388616->139670141388952 139670141389288 activation_12: Activation 139670141388952->139670141389288 139670141389344 res2c_branch2c: Conv2D 139670141389288->139670141389344 139670141389680 bn2c_branch2c: BatchNormalization 139670141389344->139670141389680 139670141389680->139670141390016 139670141390072 activation_13: Activation 139670141390016->139670141390072 139670141390128 res3a_branch2a: Conv2D 139670141390072->139670141390128 139670141391984 res3a_branch1: Conv2D 139670141390072->139670141391984 139670141390464 bn3a_branch2a: BatchNormalization 139670141390128->139670141390464 139670141353936 activation_14: Activation 139670141390464->139670141353936 139670141390920 res3a_branch2b: Conv2D 139670141353936->139670141390920 139670141391256 bn3a_branch2b: BatchNormalization 139670141390920->139670141391256 139670141391592 activation_15: Activation 139670141391256->139670141391592 139670141391648 res3a_branch2c: Conv2D 139670141391592->139670141391648 139670141392376 bn3a_branch2c: BatchNormalization 139670141391648->139670141392376 139670141392712 bn3a_branch1: BatchNormalization 139670141391984->139670141392712 139670141392992 add_5: Add 139670141392376->139670141392992 139670141392712->139670141392992 139670141393048 activation_16: Activation 139670141392992->139670141393048 139670141393104 res3b_branch2a: Conv2D 139670141393048->139670141393104 139670141440352 add_6: Add 139670141393048->139670141440352 139670141393440 bn3b_branch2a: BatchNormalization 139670141393104->139670141393440 139670141393776 activation_17: Activation 139670141393440->139670141393776 139670141393832 res3b_branch2b: Conv2D 139670141393776->139670141393832 139670141394168 bn3b_branch2b: BatchNormalization 139670141393832->139670141394168 139670141394504 activation_18: Activation 139670141394168->139670141394504 139670141394560 res3b_branch2c: Conv2D 139670141394504->139670141394560 139670141390800 bn3b_branch2c: BatchNormalization 139670141394560->139670141390800 139670141390800->139670141440352 139670141440408 activation_19: Activation 139670141440352->139670141440408 139670141440464 res3c_branch2a: Conv2D 139670141440408->139670141440464 139670141442592 add_7: Add 139670141440408->139670141442592 139670141440800 bn3c_branch2a: BatchNormalization 139670141440464->139670141440800 139670141441136 activation_20: Activation 139670141440800->139670141441136 139670141441192 res3c_branch2b: Conv2D 139670141441136->139670141441192 139670141441528 bn3c_branch2b: BatchNormalization 139670141441192->139670141441528 139670141441864 activation_21: Activation 139670141441528->139670141441864 139670141441920 res3c_branch2c: Conv2D 139670141441864->139670141441920 139670141442256 bn3c_branch2c: BatchNormalization 139670141441920->139670141442256 139670141442256->139670141442592 139670141442648 activation_22: Activation 139670141442592->139670141442648 139670141442704 res3d_branch2a: Conv2D 139670141442648->139670141442704 139670141469472 add_8: Add 139670141442648->139670141469472 139670141443040 bn3d_branch2a: BatchNormalization 139670141442704->139670141443040 139670141443376 activation_23: Activation 139670141443040->139670141443376 139670141443432 res3d_branch2b: Conv2D 139670141443376->139670141443432 139670141443768 bn3d_branch2b: BatchNormalization 139670141443432->139670141443768 139670141394896 activation_24: Activation 139670141443768->139670141394896 139670141468800 res3d_branch2c: Conv2D 139670141394896->139670141468800 139670141469136 bn3d_branch2c: BatchNormalization 139670141468800->139670141469136 139670141469136->139670141469472 139670141469528 activation_25: Activation 139670141469472->139670141469528 139670141469584 res4a_branch2a: Conv2D 139670141469528->139670141469584 139670141471376 res4a_branch1: Conv2D 139670141469528->139670141471376 139670141469920 bn4a_branch2a: BatchNormalization 139670141469584->139670141469920 139670141470256 activation_26: Activation 139670141469920->139670141470256 139670141470312 res4a_branch2b: Conv2D 139670141470256->139670141470312 139670141470648 bn4a_branch2b: BatchNormalization 139670141470312->139670141470648 139670141470984 activation_27: Activation 139670141470648->139670141470984 139670141471040 res4a_branch2c: Conv2D 139670141470984->139670141471040 139670141471768 bn4a_branch2c: BatchNormalization 139670141471040->139670141471768 139670141472104 bn4a_branch1: BatchNormalization 139670141471376->139670141472104 139670141472384 add_9: Add 139670141471768->139670141472384 139670141472104->139670141472384 139670141472440 activation_28: Activation 139670141472384->139670141472440 139670141472496 res4b_branch2a: Conv2D 139670141472440->139670141472496 139670141499264 add_10: Add 139670141472440->139670141499264 139670141444048 bn4b_branch2a: BatchNormalization 139670141472496->139670141444048 139670141497808 activation_29: Activation 139670141444048->139670141497808 139670141497864 res4b_branch2b: Conv2D 139670141497808->139670141497864 139670141498200 bn4b_branch2b: BatchNormalization 139670141497864->139670141498200 139670141498536 activation_30: Activation 139670141498200->139670141498536 139670141498592 res4b_branch2c: Conv2D 139670141498536->139670141498592 139670141498928 bn4b_branch2c: BatchNormalization 139670141498592->139670141498928 139670141498928->139670141499264 139670141499320 activation_31: Activation 139670141499264->139670141499320 139670141499376 res4c_branch2a: Conv2D 139670141499320->139670141499376 139670141472664 add_11: Add 139670141499320->139670141472664 139670141499712 bn4c_branch2a: BatchNormalization 139670141499376->139670141499712 139670141500048 activation_32: Activation 139670141499712->139670141500048 139670141500104 res4c_branch2b: Conv2D 139670141500048->139670141500104 139670141500440 bn4c_branch2b: BatchNormalization 139670141500104->139670141500440 139670141500776 activation_33: Activation 139670141500440->139670141500776 139670141500832 res4c_branch2c: Conv2D 139670141500776->139670141500832 139670141501168 bn4c_branch2c: BatchNormalization 139670141500832->139670141501168 139670141501168->139670141472664 139670141526200 activation_34: Activation 139670141472664->139670141526200 139670141526256 res4d_branch2a: Conv2D 139670141526200->139670141526256 139670141528384 add_12: Add 139670141526200->139670141528384 139670141526592 bn4d_branch2a: BatchNormalization 139670141526256->139670141526592 139670141526928 activation_35: Activation 139670141526592->139670141526928 139670141526984 res4d_branch2b: Conv2D 139670141526928->139670141526984 139670141527320 bn4d_branch2b: BatchNormalization 139670141526984->139670141527320 139670141527656 activation_36: Activation 139670141527320->139670141527656 139670141527712 res4d_branch2c: Conv2D 139670141527656->139670141527712 139670141528048 bn4d_branch2c: BatchNormalization 139670141527712->139670141528048 139670141528048->139670141528384 139670141528440 activation_37: Activation 139670141528384->139670141528440 139670141528496 res4e_branch2a: Conv2D 139670141528440->139670141528496 139670141039168 add_13: Add 139670141528440->139670141039168 139670141528832 bn4e_branch2a: BatchNormalization 139670141528496->139670141528832 139670141529168 activation_38: Activation 139670141528832->139670141529168 139670141529224 res4e_branch2b: Conv2D 139670141529168->139670141529224 139670141529560 bn4e_branch2b: BatchNormalization 139670141529224->139670141529560 139670141529896 activation_39: Activation 139670141529560->139670141529896 139670141529952 res4e_branch2c: Conv2D 139670141529896->139670141529952 139670141501392 bn4e_branch2c: BatchNormalization 139670141529952->139670141501392 139670141501392->139670141039168 139670141039224 activation_40: Activation 139670141039168->139670141039224 139670141039280 res4f_branch2a: Conv2D 139670141039224->139670141039280 139670141041408 add_14: Add 139670141039224->139670141041408 139670141039616 bn4f_branch2a: BatchNormalization 139670141039280->139670141039616 139670141039952 activation_41: Activation 139670141039616->139670141039952 139670141040008 res4f_branch2b: Conv2D 139670141039952->139670141040008 139670141040344 bn4f_branch2b: BatchNormalization 139670141040008->139670141040344 139670141040680 activation_42: Activation 139670141040344->139670141040680 139670141040736 res4f_branch2c: Conv2D 139670141040680->139670141040736 139670141041072 bn4f_branch2c: BatchNormalization 139670141040736->139670141041072 139670141041072->139670141041408 139670141041464 activation_43: Activation 139670141041408->139670141041464 139670141041520 res5a_branch2a: Conv2D 139670141041464->139670141041520 139670141067952 res5a_branch1: Conv2D 139670141041464->139670141067952 139670141041856 bn5a_branch2a: BatchNormalization 139670141041520->139670141041856 139670141042192 activation_44: Activation 139670141041856->139670141042192 139670141042248 res5a_branch2b: Conv2D 139670141042192->139670141042248 139670141530064 bn5a_branch2b: BatchNormalization 139670141042248->139670141530064 139670141067560 activation_45: Activation 139670141530064->139670141067560 139670141067616 res5a_branch2c: Conv2D 139670141067560->139670141067616 139670141068344 bn5a_branch2c: BatchNormalization 139670141067616->139670141068344 139670141068680 bn5a_branch1: BatchNormalization 139670141067952->139670141068680 139670141068960 add_15: Add 139670141068344->139670141068960 139670141068680->139670141068960 139670141069016 activation_46: Activation 139670141068960->139670141069016 139670141069072 res5b_branch2a: Conv2D 139670141069016->139670141069072 139670141071200 add_16: Add 139670141069016->139670141071200 139670141069408 bn5b_branch2a: BatchNormalization 139670141069072->139670141069408 139670141069744 activation_47: Activation 139670141069408->139670141069744 139670141069800 res5b_branch2b: Conv2D 139670141069744->139670141069800 139670141070136 bn5b_branch2b: BatchNormalization 139670141069800->139670141070136 139670141070472 activation_48: Activation 139670141070136->139670141070472 139670141070528 res5b_branch2c: Conv2D 139670141070472->139670141070528 139670141070864 bn5b_branch2c: BatchNormalization 139670141070528->139670141070864 139670141070864->139670141071200 139670141071256 activation_49: Activation 139670141071200->139670141071256 139670141042584 res5c_branch2a: Conv2D 139670141071256->139670141042584 139670141102176 add_17: Add 139670141071256->139670141102176 139670141100384 bn5c_branch2a: BatchNormalization 139670141042584->139670141100384 139670141100720 activation_50: Activation 139670141100384->139670141100720 139670141100776 res5c_branch2b: Conv2D 139670141100720->139670141100776 139670141101112 bn5c_branch2b: BatchNormalization 139670141100776->139670141101112 139670141101448 activation_51: Activation 139670141101112->139670141101448 139670141101504 res5c_branch2c: Conv2D 139670141101448->139670141101504 139670141101840 bn5c_branch2c: BatchNormalization 139670141101504->139670141101840 139670141101840->139670141102176 139670141102232 activation_52: Activation 139670141102176->139670141102232 139670141102288 avg_pool: AveragePooling2D 139670141102232->139670141102288 139670141102456 flatten_1: Flatten 139670141102288->139670141102456 139670141102568 fc6: Dense 139670141102456->139670141102568

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: