Coursera

Residual Networks

Welcome to the first assignment of this week! You’ll be building a very deep convolutional network, 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 feasible.

By the end of this assignment, you’ll be able to:

Implement the basic building blocks of ResNets in a deep neural network using Keras
Put together these building blocks to implement and train a state-of-the-art neural network for image classification
Implement a skip connection in your network

For this assignment, you’ll use Keras.

Before jumping into the problem, run the cell below to load the required packages.

Important Note on Submission to the AutoGrader

Before submitting your assignment to the AutoGrader, please make sure you are not doing the following:

You have not added any extra print statement(s) in the assignment.
You have not added any extra code cell(s) in the assignment.
You have not changed any of the function parameters.
You are not using any global variables inside your graded exercises. Unless specifically instructed to do so, please refrain from it and use the local variables instead.
You are not changing the assignment code where it is not required, like creating extra variables.

If you do any of the following, you will get something like, Grader not found (or similarly unexpected) error upon submitting your assignment. Before asking for help/debugging the errors in your assignment, check for these first. If this is the case, and you don’t remember the changes you have made, you can get a fresh copy of the assignment by following these instructions.

Table of Content

1 - Packages
2 - The Problem of Very Deep Neural Networks
3 - Building a Residual Network
- 3.1 - The Identity Block
  - Exercise 1 - identity_block
- 3.2 - The Convolutional Block
  - Exercise 2 - convolutional_block
4 - Building Your First ResNet Model (50 layers)
- Exercise 3 - ResNet50
5 - Test on Your Own Image (Optional/Ungraded)
6 - Bibliography

1 - Packages

import tensorflow as tf
import numpy as np
import scipy.misc
from tensorflow.keras.applications.resnet_v2 import ResNet50V2
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet_v2 import preprocess_input, decode_predictions
from tensorflow.keras import layers
from tensorflow.keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D
from tensorflow.keras.models import Model, load_model
from resnets_utils import *
from tensorflow.keras.initializers import random_uniform, glorot_uniform, constant, identity
from tensorflow.python.framework.ops import EagerTensor
from matplotlib.pyplot import imshow

from test_utils import summary, comparator
import public_tests

%matplotlib inline

2 - The Problem of Very Deep Neural Networks

Last week, you built your first convolutional neural networks: first manually with numpy, then using Tensorflow and Keras.

In recent years, neural networks have become much deeper, with state-of-the-art networks evolving from having 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 shallower layers, closer to the input) to very complex features (at the deeper layers, closer to the output).
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 prohibitively slow.
More specifically, during gradient descent, as you backpropagate 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,” from gaining very large values).
During training, you might therefore see the magnitude (or norm) of the gradient for the shallower layers decrease to zero very rapidly as training proceeds, as shown below:

Figure 1 : Vanishing gradient
The speed of learning decreases very rapidly for the shallower layers as the network trains

Not to worry! You are now going to solve this problem by building a Residual Network!

3 - 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.

The lecture mentioned 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.

On that note, there is also some evidence that the ease of learning an identity function accounts for ResNets’ remarkable performance even more than skip connections help with vanishing gradients.

Two main types of blocks are used in a ResNet, depending mainly on whether the input/output dimensions are the same or different. You are going to implement both of them: the “identity block” and the “convolutional block.”

3.1 - 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, notice the CONV2D and ReLU steps in each layer. To speed up training, a BatchNorm step has been added. 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.

These are the individual steps:

First component of main path:

The first CONV2D has $F_1$ filters of shape (1,1) and a stride of (1,1). Its padding is “valid”. Use 0 as the seed for the random uniform initialization: kernel_initializer = initializer(seed=0).
The first BatchNorm is normalizing the ‘channels’ axis.
Then apply the ReLU activation function. This has no hyperparameters.

Second component of main path:

The second CONV2D has $F_2$ filters of shape $(f,f)$ and a stride of (1,1). Its padding is “same”. Use 0 as the seed for the random uniform initialization: kernel_initializer = initializer(seed=0).
The second BatchNorm is normalizing the ‘channels’ axis.
Then apply the ReLU activation function. This has no hyperparameters.

Third component of main path:

The third CONV2D has $F_3$ filters of shape (1,1) and a stride of (1,1). Its padding is “valid”. Use 0 as the seed for the random uniform initialization: kernel_initializer = initializer(seed=0).
The third BatchNorm is normalizing the ‘channels’ axis.
Note that there is no ReLU activation function in this component.

Final step:

The X_shortcut and the output from the 3rd layer X are added together.
Hint: The syntax will look something like Add()([var1,var2])
Then apply the ReLU activation function. This has no hyperparameters.

Exercise 1 - identity_block

Implement the ResNet identity block. The first component of the main path has been implemented for you already! First, you should read these docs carefully to make sure you understand what’s happening. Then, implement the rest.

To implement the Conv2D step: Conv2D
To implement BatchNorm: BatchNormalization BatchNormalization(axis = 3)(X, training = training). If training is set to False, its weights are not updated with the new examples. I.e when the model is used in prediction mode.
For the activation, use: Activation('relu')(X)
To add the value passed forward by the shortcut: Add

We have added the initializer argument to our functions. This parameter receives an initializer function like the ones included in the package tensorflow.keras.initializers or any other custom initializer. By default it will be set to random_uniform

Remember that these functions accept a seed argument that can be any value you want, but that in this notebook must set to 0 for grading purposes.

Here is where you’re actually using the power of the Functional API to create a shortcut path:

# UNQ_C1
# GRADED FUNCTION: identity_block

def identity_block(X, f, filters, training=True, initializer=random_uniform):
    """
    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
    training -- True: Behave in training mode
                False: Behave in inference mode
    initializer -- to set up the initial weights of a layer. Equals to random uniform initializer
    
    Returns:
    X -- output of the identity block, tensor of shape (m, n_H, n_W, n_C)
    """
    
    # 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, strides = (1,1), padding = 'valid', kernel_initializer = initializer(seed=0))(X)
    X = BatchNormalization(axis = 3)(X, training = training) # Default axis
    X = Activation('relu')(X)
    
    ### START CODE HERE
    ## Second component of main path (≈3 lines)
    ## Set the padding = 'same'
    X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1, 1), padding = "same", kernel_initializer = initializer(seed=0))(X)
    X = BatchNormalization(axis = 3)(X, training = training)
    X = Activation("relu")(X) 

    ## Third component of main path (≈2 lines)
    ## Set the padding = 'valid'
    X = Conv2D(filters = F3, kernel_size = 1, strides = (1, 1), padding = "valid", kernel_initializer = initializer(seed=0))(X)
    X = BatchNormalization(axis = 3)(X, training = training) 
    
    ## Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)
    X = Add()([X_shortcut, X])
    X = Activation("relu")(X)
    ### END CODE HERE

    return X

np.random.seed(1)
X1 = np.ones((1, 4, 4, 3)) * -1
X2 = np.ones((1, 4, 4, 3)) * 1
X3 = np.ones((1, 4, 4, 3)) * 3

X = np.concatenate((X1, X2, X3), axis = 0).astype(np.float32)

A3 = identity_block(X, f=2, filters=[4, 4, 3],
                   initializer=lambda seed=0:constant(value=1),
                   training=False)
print('\033[1mWith training=False\033[0m\n')
A3np = A3.numpy()
print(np.around(A3.numpy()[:,(0,-1),:,:].mean(axis = 3), 5))
resume = A3np[:,(0,-1),:,:].mean(axis = 3)
print(resume[1, 1, 0])

print('\n\033[1mWith training=True\033[0m\n')
np.random.seed(1)
A4 = identity_block(X, f=2, filters=[3, 3, 3],
                   initializer=lambda seed=0:constant(value=1),
                   training=True)
print(np.around(A4.numpy()[:,(0,-1),:,:].mean(axis = 3), 5))

public_tests.identity_block_test(identity_block)

[1mWith training=False[0m

[[[  0.        0.        0.        0.     ]
  [  0.        0.        0.        0.     ]]

 [[192.71234 192.71234 192.71234  96.85617]
  [ 96.85617  96.85617  96.85617  48.92808]]

 [[578.1371  578.1371  578.1371  290.5685 ]
  [290.5685  290.5685  290.5685  146.78426]]]
96.85617

[1mWith training=True[0m

[[[0.      0.      0.      0.     ]
  [0.      0.      0.      0.     ]]

 [[0.40739 0.40739 0.40739 0.40739]
  [0.40739 0.40739 0.40739 0.40739]]

 [[4.99991 4.99991 4.99991 3.25948]
  [3.25948 3.25948 3.25948 2.40739]]]
[32mAll tests passed![0m

Expected value

With training=False

[[[  0.        0.        0.        0.     ]
  [  0.        0.        0.        0.     ]]

 [[192.71234 192.71234 192.71234  96.85617]
  [ 96.85617  96.85617  96.85617  48.92808]]

 [[578.1371  578.1371  578.1371  290.5685 ]
  [290.5685  290.5685  290.5685  146.78426]]]
96.85617

With training=True

[[[0.      0.      0.      0.     ]
  [0.      0.      0.      0.     ]]

 [[0.40739 0.40739 0.40739 0.40739]
  [0.40739 0.40739 0.40739 0.40739]]

 [[4.99991 4.99991 4.99991 3.25948]
  [3.25948 3.25948 3.25948 2.40739]]]

3.2 - 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 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 $W_s$ 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.
As for the previous exercise, the additional initializer argument is required for grading purposes, and it has been set by default to glorot_uniform

The details of the convolutional block are as follows.

First component of main path:

The first CONV2D has $F_1$ filters of shape (1,1) and a stride of (s,s). Its padding is “valid”. Use 0 as the glorot_uniform seed kernel_initializer = initializer(seed=0).
The first BatchNorm is normalizing the ‘channels’ axis.
Then apply the ReLU activation function. This has no hyperparameters.

Second component of main path:

The second CONV2D has $F_2$ filters of shape (f,f) and a stride of (1,1). Its padding is “same”. Use 0 as the glorot_uniform seed kernel_initializer = initializer(seed=0).
The second BatchNorm is normalizing the ‘channels’ axis.
Then apply the ReLU activation function. This has no hyperparameters.

Third component of main path:

The third CONV2D has $F_3$ filters of shape (1,1) and a stride of (1,1). Its padding is “valid”. Use 0 as the glorot_uniform seed kernel_initializer = initializer(seed=0).
The third BatchNorm is normalizing the ‘channels’ axis. Note that there is no ReLU activation function in this component.

Shortcut path:

The CONV2D has $F_3$ filters of shape (1,1) and a stride of (s,s). Its padding is “valid”. Use 0 as the glorot_uniform seed kernel_initializer = initializer(seed=0).
The BatchNorm is normalizing the ‘channels’ axis.

Final step:

The shortcut and the main path values are added together.
Then apply the ReLU activation function. This has no hyperparameters.

Exercise 2 - convolutional_block

Implement the convolutional block. The first component of the main path is already implemented; then it’s your turn to implement the rest! As before, always use 0 as the seed for the random initialization, to ensure consistency with the grader.

Conv2D
BatchNormalization (axis: Integer, the axis that should be normalized (typically the features axis)) BatchNormalization(axis = 3)(X, training = training). If training is set to False, its weights are not updated with the new examples. I.e when the model is used in prediction mode.
For the activation, use: Activation('relu')(X)
Add

Remember that these functions accept a seed argument that can be any value you want, but that in this notebook must set to 0 for grading purposes.

# UNQ_C2
# GRADED FUNCTION: convolutional_block

def convolutional_block(X, f, filters, s = 2, training=True, initializer=glorot_uniform):
    """
    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
    s -- Integer, specifying the stride to be used
    training -- True: Behave in training mode
                False: Behave in inference mode
    initializer -- to set up the initial weights of a layer. Equals to Glorot uniform initializer, 
                   also called Xavier uniform initializer.
    
    Returns:
    X -- output of the convolutional block, tensor of shape (m, n_H, n_W, n_C)
    """
    
    # Retrieve Filters
    F1, F2, F3 = filters
    
    # Save the input value
    X_shortcut = X


    ##### MAIN PATH #####
    
    # First component of main path glorot_uniform(seed=0)
    X = Conv2D(filters = F1, kernel_size = 1, strides = (s, s), padding='valid', kernel_initializer = initializer(seed=0))(X)
    X = BatchNormalization(axis = 3)(X, training=training)
    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", kernel_initializer = initializer(seed=0))(X) 
    X = BatchNormalization(axis = 3)(X, training=training) 
    X = Activation('relu')(X) 

    ## Third component of main path (≈2 lines)
    X = Conv2D(filters = F3, kernel_size = 1, strides = (1, 1), padding="valid", kernel_initializer = initializer(seed=0))(X) 
    X = BatchNormalization(axis = 3)(X, training=training)  
    
    ##### SHORTCUT PATH ##### (≈2 lines)
    X_shortcut = Conv2D(filters = F3, kernel_size = 1, strides = (s, s), padding="valid", kernel_initializer = initializer(seed=0))(X_shortcut)
    X_shortcut = BatchNormalization(axis = 3)(X_shortcut, training=training) 
    
    ### END CODE HERE

    # Final step: Add shortcut value to main path (Use this order [X, X_shortcut]), and pass it through a RELU activation
    X = Add()([X, X_shortcut])
    X = Activation('relu')(X)
    
    return X

from outputs import convolutional_block_output1, convolutional_block_output2
np.random.seed(1)
#X = np.random.randn(3, 4, 4, 6).astype(np.float32)
X1 = np.ones((1, 4, 4, 3)) * -1
X2 = np.ones((1, 4, 4, 3)) * 1
X3 = np.ones((1, 4, 4, 3)) * 3

X = np.concatenate((X1, X2, X3), axis = 0).astype(np.float32)

A = convolutional_block(X, f = 2, filters = [2, 4, 6], training=False)

assert type(A) == EagerTensor, "Use only tensorflow and keras functions"
assert tuple(tf.shape(A).numpy()) == (3, 2, 2, 6), "Wrong shape."
assert np.allclose(A.numpy(), convolutional_block_output1), "Wrong values when training=False."
print(A[0])

B = convolutional_block(X, f = 2, filters = [2, 4, 6], training=True)
assert np.allclose(B.numpy(), convolutional_block_output2), "Wrong values when training=True."

print('\033[92mAll tests passed!')

tf.Tensor(
[[[0.         0.66683817 0.         0.         0.88853896 0.5274254 ]
  [0.         0.65053666 0.         0.         0.89592844 0.49965227]]

 [[0.         0.6312079  0.         0.         0.8636247  0.47643146]
  [0.         0.5688321  0.         0.         0.85534114 0.41709304]]], shape=(2, 2, 6), dtype=float32)
[92mAll tests passed!

Expected value

tf.Tensor(
[[[0.         0.66683817 0.         0.         0.88853896 0.5274254 ]
  [0.         0.65053666 0.         0.         0.89592844 0.49965227]]

 [[0.         0.6312079  0.         0.         0.8636247  0.47643146]
  [0.         0.5688321  0.         0.         0.85534114 0.41709304]]], shape=(2, 2, 6), dtype=float32)

4 - 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).
- BatchNorm is applied to the ‘channels’ axis of the input.
- ReLU activation is applied.
- MaxPooling uses a (3,3) window and a (2,2) stride.
Stage 2:
- The convolutional block uses three sets of filters of size [64,64,256], “f” is 3, and “s” is 1.
- The 2 identity blocks use three sets of filters of size [64,64,256], and “f” is 3.
Stage 3:
- The convolutional block uses three sets of filters of size [128,128,512], “f” is 3 and “s” is 2.
- The 3 identity blocks use three sets of filters of size [128,128,512] and “f” is 3.
Stage 4:
- The convolutional block uses three sets of filters of size [256, 256, 1024], “f” is 3 and “s” is 2.
- The 5 identity blocks use three sets of filters of size [256, 256, 1024] and “f” is 3.
Stage 5:
- The convolutional block uses three sets of filters of size [512, 512, 2048], “f” is 3 and “s” is 2.
- The 2 identity blocks use three sets of filters of size [512, 512, 2048] and “f” is 3.
The 2D Average Pooling uses a window of shape (2,2).
The ‘flatten’ layer doesn’t have any hyperparameters.
The Fully Connected (Dense) layer reduces its input to the number of classes using a softmax activation.

Exercise 3 - ResNet50

Implement the ResNet with 50 layers described in the figure above. We have implemented Stages 1 and 2. Please implement the rest. (The syntax for implementing Stages 3-5 should be quite similar to that of Stage 2) Make sure you follow the naming convention in the text above.

You’ll need to use this function:

Average pooling see reference

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

Conv2D: See reference
BatchNorm: See reference (axis: Integer, the axis that should be normalized (typically the features axis))
Zero padding: See reference
Max pooling: See reference
Fully connected layer: See reference
Addition: See reference

# UNQ_C3
# GRADED FUNCTION: ResNet50

def ResNet50(input_shape = (64, 64, 3), classes = 6):
    """
    Stage-wise implementation of the architecture of the popular ResNet50:
    CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3
    -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> FLATTEN -> DENSE 

    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), kernel_initializer = glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis = 3)(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], s = 1)
    X = identity_block(X, 3, [64, 64, 256])
    X = identity_block(X, 3, [64, 64, 256])

    ### START CODE HERE
    
    ## Stage 3 (≈4 lines)
    X = convolutional_block(X, f = 3, filters = [128, 128, 512], s = 2)
    X = identity_block(X, 3, [128, 128, 512]) 
    X = identity_block(X, 3, [128, 128, 512])
    X = identity_block(X, 3, [128, 128, 512]) 
    
    ## Stage 4 (≈6 lines)
    X = convolutional_block(X, f = 3, filters = [256, 256, 1024], s = 2) 
    X = identity_block(X, 3, [256, 256, 1024]) 
    X = identity_block(X, 3, [256, 256, 1024]) 
    X = identity_block(X, 3, [256, 256, 1024]) 
    X = identity_block(X, 3, [256, 256, 1024]) 
    X = identity_block(X, 3, [256, 256, 1024]) 

    ## Stage 5 (≈3 lines)
    X = convolutional_block(X, f = 3, filters = [512, 512, 2048], s = 2) 
    X = identity_block(X, 3, [512, 512, 2048]) 
    X = identity_block(X, 3, [512, 512, 2048]) 

    ## AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)"
    X = AveragePooling2D((2, 2))(X) 
    
    ### END CODE HERE

    # output layer
    X = Flatten()(X)
    X = Dense(classes, activation='softmax', kernel_initializer = glorot_uniform(seed=0))(X)
    
    
    # Create model
    model = Model(inputs = X_input, outputs = X)

    return model

Run the following code to build the model’s graph. If your implementation is incorrect, you’ll know it by checking your accuracy when running model.fit(...) below.

model = ResNet50(input_shape = (64, 64, 3), classes = 6)
print(model.summary())

Model: "functional_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 64, 64, 3)]  0                                            
__________________________________________________________________________________________________
zero_padding2d (ZeroPadding2D)  (None, 70, 70, 3)    0           input_1[0][0]                    
__________________________________________________________________________________________________
conv2d_28 (Conv2D)              (None, 32, 32, 64)   9472        zero_padding2d[0][0]             
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, 32, 32, 64)   256         conv2d_28[0][0]                  
__________________________________________________________________________________________________
activation_24 (Activation)      (None, 32, 32, 64)   0           batch_normalization_28[0][0]     
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D)    (None, 15, 15, 64)   0           activation_24[0][0]              
__________________________________________________________________________________________________
conv2d_29 (Conv2D)              (None, 15, 15, 64)   4160        max_pooling2d[0][0]              
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, 15, 15, 64)   256         conv2d_29[0][0]                  
__________________________________________________________________________________________________
activation_25 (Activation)      (None, 15, 15, 64)   0           batch_normalization_29[0][0]     
__________________________________________________________________________________________________
conv2d_30 (Conv2D)              (None, 15, 15, 64)   36928       activation_25[0][0]              
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, 15, 15, 64)   256         conv2d_30[0][0]                  
__________________________________________________________________________________________________
activation_26 (Activation)      (None, 15, 15, 64)   0           batch_normalization_30[0][0]     
__________________________________________________________________________________________________
conv2d_31 (Conv2D)              (None, 15, 15, 256)  16640       activation_26[0][0]              
__________________________________________________________________________________________________
conv2d_32 (Conv2D)              (None, 15, 15, 256)  16640       max_pooling2d[0][0]              
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, 15, 15, 256)  1024        conv2d_31[0][0]                  
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, 15, 15, 256)  1024        conv2d_32[0][0]                  
__________________________________________________________________________________________________
add_8 (Add)                     (None, 15, 15, 256)  0           batch_normalization_31[0][0]     
                                                                 batch_normalization_32[0][0]     
__________________________________________________________________________________________________
activation_27 (Activation)      (None, 15, 15, 256)  0           add_8[0][0]                      
__________________________________________________________________________________________________
conv2d_33 (Conv2D)              (None, 15, 15, 64)   16448       activation_27[0][0]              
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, 15, 15, 64)   256         conv2d_33[0][0]                  
__________________________________________________________________________________________________
activation_28 (Activation)      (None, 15, 15, 64)   0           batch_normalization_33[0][0]     
__________________________________________________________________________________________________
conv2d_34 (Conv2D)              (None, 15, 15, 64)   36928       activation_28[0][0]              
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, 15, 15, 64)   256         conv2d_34[0][0]                  
__________________________________________________________________________________________________
activation_29 (Activation)      (None, 15, 15, 64)   0           batch_normalization_34[0][0]     
__________________________________________________________________________________________________
conv2d_35 (Conv2D)              (None, 15, 15, 256)  16640       activation_29[0][0]              
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, 15, 15, 256)  1024        conv2d_35[0][0]                  
__________________________________________________________________________________________________
add_9 (Add)                     (None, 15, 15, 256)  0           activation_27[0][0]              
                                                                 batch_normalization_35[0][0]     
__________________________________________________________________________________________________
activation_30 (Activation)      (None, 15, 15, 256)  0           add_9[0][0]                      
__________________________________________________________________________________________________
conv2d_36 (Conv2D)              (None, 15, 15, 64)   16448       activation_30[0][0]              
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, 15, 15, 64)   256         conv2d_36[0][0]                  
__________________________________________________________________________________________________
activation_31 (Activation)      (None, 15, 15, 64)   0           batch_normalization_36[0][0]     
__________________________________________________________________________________________________
conv2d_37 (Conv2D)              (None, 15, 15, 64)   36928       activation_31[0][0]              
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, 15, 15, 64)   256         conv2d_37[0][0]                  
__________________________________________________________________________________________________
activation_32 (Activation)      (None, 15, 15, 64)   0           batch_normalization_37[0][0]     
__________________________________________________________________________________________________
conv2d_38 (Conv2D)              (None, 15, 15, 256)  16640       activation_32[0][0]              
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, 15, 15, 256)  1024        conv2d_38[0][0]                  
__________________________________________________________________________________________________
add_10 (Add)                    (None, 15, 15, 256)  0           activation_30[0][0]              
                                                                 batch_normalization_38[0][0]     
__________________________________________________________________________________________________
activation_33 (Activation)      (None, 15, 15, 256)  0           add_10[0][0]                     
__________________________________________________________________________________________________
conv2d_39 (Conv2D)              (None, 8, 8, 128)    32896       activation_33[0][0]              
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, 8, 8, 128)    512         conv2d_39[0][0]                  
__________________________________________________________________________________________________
activation_34 (Activation)      (None, 8, 8, 128)    0           batch_normalization_39[0][0]     
__________________________________________________________________________________________________
conv2d_40 (Conv2D)              (None, 8, 8, 128)    147584      activation_34[0][0]              
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, 8, 8, 128)    512         conv2d_40[0][0]                  
__________________________________________________________________________________________________
activation_35 (Activation)      (None, 8, 8, 128)    0           batch_normalization_40[0][0]     
__________________________________________________________________________________________________
conv2d_41 (Conv2D)              (None, 8, 8, 512)    66048       activation_35[0][0]              
__________________________________________________________________________________________________
conv2d_42 (Conv2D)              (None, 8, 8, 512)    131584      activation_33[0][0]              
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, 8, 8, 512)    2048        conv2d_41[0][0]                  
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, 8, 8, 512)    2048        conv2d_42[0][0]                  
__________________________________________________________________________________________________
add_11 (Add)                    (None, 8, 8, 512)    0           batch_normalization_41[0][0]     
                                                                 batch_normalization_42[0][0]     
__________________________________________________________________________________________________
activation_36 (Activation)      (None, 8, 8, 512)    0           add_11[0][0]                     
__________________________________________________________________________________________________
conv2d_43 (Conv2D)              (None, 8, 8, 128)    65664       activation_36[0][0]              
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, 8, 8, 128)    512         conv2d_43[0][0]                  
__________________________________________________________________________________________________
activation_37 (Activation)      (None, 8, 8, 128)    0           batch_normalization_43[0][0]     
__________________________________________________________________________________________________
conv2d_44 (Conv2D)              (None, 8, 8, 128)    147584      activation_37[0][0]              
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, 8, 8, 128)    512         conv2d_44[0][0]                  
__________________________________________________________________________________________________
activation_38 (Activation)      (None, 8, 8, 128)    0           batch_normalization_44[0][0]     
__________________________________________________________________________________________________
conv2d_45 (Conv2D)              (None, 8, 8, 512)    66048       activation_38[0][0]              
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, 8, 8, 512)    2048        conv2d_45[0][0]                  
__________________________________________________________________________________________________
add_12 (Add)                    (None, 8, 8, 512)    0           activation_36[0][0]              
                                                                 batch_normalization_45[0][0]     
__________________________________________________________________________________________________
activation_39 (Activation)      (None, 8, 8, 512)    0           add_12[0][0]                     
__________________________________________________________________________________________________
conv2d_46 (Conv2D)              (None, 8, 8, 128)    65664       activation_39[0][0]              
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, 8, 8, 128)    512         conv2d_46[0][0]                  
__________________________________________________________________________________________________
activation_40 (Activation)      (None, 8, 8, 128)    0           batch_normalization_46[0][0]     
__________________________________________________________________________________________________
conv2d_47 (Conv2D)              (None, 8, 8, 128)    147584      activation_40[0][0]              
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, 8, 8, 128)    512         conv2d_47[0][0]                  
__________________________________________________________________________________________________
activation_41 (Activation)      (None, 8, 8, 128)    0           batch_normalization_47[0][0]     
__________________________________________________________________________________________________
conv2d_48 (Conv2D)              (None, 8, 8, 512)    66048       activation_41[0][0]              
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, 8, 8, 512)    2048        conv2d_48[0][0]                  
__________________________________________________________________________________________________
add_13 (Add)                    (None, 8, 8, 512)    0           activation_39[0][0]              
                                                                 batch_normalization_48[0][0]     
__________________________________________________________________________________________________
activation_42 (Activation)      (None, 8, 8, 512)    0           add_13[0][0]                     
__________________________________________________________________________________________________
conv2d_49 (Conv2D)              (None, 8, 8, 128)    65664       activation_42[0][0]              
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, 8, 8, 128)    512         conv2d_49[0][0]                  
__________________________________________________________________________________________________
activation_43 (Activation)      (None, 8, 8, 128)    0           batch_normalization_49[0][0]     
__________________________________________________________________________________________________
conv2d_50 (Conv2D)              (None, 8, 8, 128)    147584      activation_43[0][0]              
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, 8, 8, 128)    512         conv2d_50[0][0]                  
__________________________________________________________________________________________________
activation_44 (Activation)      (None, 8, 8, 128)    0           batch_normalization_50[0][0]     
__________________________________________________________________________________________________
conv2d_51 (Conv2D)              (None, 8, 8, 512)    66048       activation_44[0][0]              
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, 8, 8, 512)    2048        conv2d_51[0][0]                  
__________________________________________________________________________________________________
add_14 (Add)                    (None, 8, 8, 512)    0           activation_42[0][0]              
                                                                 batch_normalization_51[0][0]     
__________________________________________________________________________________________________
activation_45 (Activation)      (None, 8, 8, 512)    0           add_14[0][0]                     
__________________________________________________________________________________________________
conv2d_52 (Conv2D)              (None, 4, 4, 256)    131328      activation_45[0][0]              
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, 4, 4, 256)    1024        conv2d_52[0][0]                  
__________________________________________________________________________________________________
activation_46 (Activation)      (None, 4, 4, 256)    0           batch_normalization_52[0][0]     
__________________________________________________________________________________________________
conv2d_53 (Conv2D)              (None, 4, 4, 256)    590080      activation_46[0][0]              
__________________________________________________________________________________________________
batch_normalization_53 (BatchNo (None, 4, 4, 256)    1024        conv2d_53[0][0]                  
__________________________________________________________________________________________________
activation_47 (Activation)      (None, 4, 4, 256)    0           batch_normalization_53[0][0]     
__________________________________________________________________________________________________
conv2d_54 (Conv2D)              (None, 4, 4, 1024)   263168      activation_47[0][0]              
__________________________________________________________________________________________________
conv2d_55 (Conv2D)              (None, 4, 4, 1024)   525312      activation_45[0][0]              
__________________________________________________________________________________________________
batch_normalization_54 (BatchNo (None, 4, 4, 1024)   4096        conv2d_54[0][0]                  
__________________________________________________________________________________________________
batch_normalization_55 (BatchNo (None, 4, 4, 1024)   4096        conv2d_55[0][0]                  
__________________________________________________________________________________________________
add_15 (Add)                    (None, 4, 4, 1024)   0           batch_normalization_54[0][0]     
                                                                 batch_normalization_55[0][0]     
__________________________________________________________________________________________________
activation_48 (Activation)      (None, 4, 4, 1024)   0           add_15[0][0]                     
__________________________________________________________________________________________________
conv2d_56 (Conv2D)              (None, 4, 4, 256)    262400      activation_48[0][0]              
__________________________________________________________________________________________________
batch_normalization_56 (BatchNo (None, 4, 4, 256)    1024        conv2d_56[0][0]                  
__________________________________________________________________________________________________
activation_49 (Activation)      (None, 4, 4, 256)    0           batch_normalization_56[0][0]     
__________________________________________________________________________________________________
conv2d_57 (Conv2D)              (None, 4, 4, 256)    590080      activation_49[0][0]              
__________________________________________________________________________________________________
batch_normalization_57 (BatchNo (None, 4, 4, 256)    1024        conv2d_57[0][0]                  
__________________________________________________________________________________________________
activation_50 (Activation)      (None, 4, 4, 256)    0           batch_normalization_57[0][0]     
__________________________________________________________________________________________________
conv2d_58 (Conv2D)              (None, 4, 4, 1024)   263168      activation_50[0][0]              
__________________________________________________________________________________________________
batch_normalization_58 (BatchNo (None, 4, 4, 1024)   4096        conv2d_58[0][0]                  
__________________________________________________________________________________________________
add_16 (Add)                    (None, 4, 4, 1024)   0           activation_48[0][0]              
                                                                 batch_normalization_58[0][0]     
__________________________________________________________________________________________________
activation_51 (Activation)      (None, 4, 4, 1024)   0           add_16[0][0]                     
__________________________________________________________________________________________________
conv2d_59 (Conv2D)              (None, 4, 4, 256)    262400      activation_51[0][0]              
__________________________________________________________________________________________________
batch_normalization_59 (BatchNo (None, 4, 4, 256)    1024        conv2d_59[0][0]                  
__________________________________________________________________________________________________
activation_52 (Activation)      (None, 4, 4, 256)    0           batch_normalization_59[0][0]     
__________________________________________________________________________________________________
conv2d_60 (Conv2D)              (None, 4, 4, 256)    590080      activation_52[0][0]              
__________________________________________________________________________________________________
batch_normalization_60 (BatchNo (None, 4, 4, 256)    1024        conv2d_60[0][0]                  
__________________________________________________________________________________________________
activation_53 (Activation)      (None, 4, 4, 256)    0           batch_normalization_60[0][0]     
__________________________________________________________________________________________________
conv2d_61 (Conv2D)              (None, 4, 4, 1024)   263168      activation_53[0][0]              
__________________________________________________________________________________________________
batch_normalization_61 (BatchNo (None, 4, 4, 1024)   4096        conv2d_61[0][0]                  
__________________________________________________________________________________________________
add_17 (Add)                    (None, 4, 4, 1024)   0           activation_51[0][0]              
                                                                 batch_normalization_61[0][0]     
__________________________________________________________________________________________________
activation_54 (Activation)      (None, 4, 4, 1024)   0           add_17[0][0]                     
__________________________________________________________________________________________________
conv2d_62 (Conv2D)              (None, 4, 4, 256)    262400      activation_54[0][0]              
__________________________________________________________________________________________________
batch_normalization_62 (BatchNo (None, 4, 4, 256)    1024        conv2d_62[0][0]                  
__________________________________________________________________________________________________
activation_55 (Activation)      (None, 4, 4, 256)    0           batch_normalization_62[0][0]     
__________________________________________________________________________________________________
conv2d_63 (Conv2D)              (None, 4, 4, 256)    590080      activation_55[0][0]              
__________________________________________________________________________________________________
batch_normalization_63 (BatchNo (None, 4, 4, 256)    1024        conv2d_63[0][0]                  
__________________________________________________________________________________________________
activation_56 (Activation)      (None, 4, 4, 256)    0           batch_normalization_63[0][0]     
__________________________________________________________________________________________________
conv2d_64 (Conv2D)              (None, 4, 4, 1024)   263168      activation_56[0][0]              
__________________________________________________________________________________________________
batch_normalization_64 (BatchNo (None, 4, 4, 1024)   4096        conv2d_64[0][0]                  
__________________________________________________________________________________________________
add_18 (Add)                    (None, 4, 4, 1024)   0           activation_54[0][0]              
                                                                 batch_normalization_64[0][0]     
__________________________________________________________________________________________________
activation_57 (Activation)      (None, 4, 4, 1024)   0           add_18[0][0]                     
__________________________________________________________________________________________________
conv2d_65 (Conv2D)              (None, 4, 4, 256)    262400      activation_57[0][0]              
__________________________________________________________________________________________________
batch_normalization_65 (BatchNo (None, 4, 4, 256)    1024        conv2d_65[0][0]                  
__________________________________________________________________________________________________
activation_58 (Activation)      (None, 4, 4, 256)    0           batch_normalization_65[0][0]     
__________________________________________________________________________________________________
conv2d_66 (Conv2D)              (None, 4, 4, 256)    590080      activation_58[0][0]              
__________________________________________________________________________________________________
batch_normalization_66 (BatchNo (None, 4, 4, 256)    1024        conv2d_66[0][0]                  
__________________________________________________________________________________________________
activation_59 (Activation)      (None, 4, 4, 256)    0           batch_normalization_66[0][0]     
__________________________________________________________________________________________________
conv2d_67 (Conv2D)              (None, 4, 4, 1024)   263168      activation_59[0][0]              
__________________________________________________________________________________________________
batch_normalization_67 (BatchNo (None, 4, 4, 1024)   4096        conv2d_67[0][0]                  
__________________________________________________________________________________________________
add_19 (Add)                    (None, 4, 4, 1024)   0           activation_57[0][0]              
                                                                 batch_normalization_67[0][0]     
__________________________________________________________________________________________________
activation_60 (Activation)      (None, 4, 4, 1024)   0           add_19[0][0]                     
__________________________________________________________________________________________________
conv2d_68 (Conv2D)              (None, 4, 4, 256)    262400      activation_60[0][0]              
__________________________________________________________________________________________________
batch_normalization_68 (BatchNo (None, 4, 4, 256)    1024        conv2d_68[0][0]                  
__________________________________________________________________________________________________
activation_61 (Activation)      (None, 4, 4, 256)    0           batch_normalization_68[0][0]     
__________________________________________________________________________________________________
conv2d_69 (Conv2D)              (None, 4, 4, 256)    590080      activation_61[0][0]              
__________________________________________________________________________________________________
batch_normalization_69 (BatchNo (None, 4, 4, 256)    1024        conv2d_69[0][0]                  
__________________________________________________________________________________________________
activation_62 (Activation)      (None, 4, 4, 256)    0           batch_normalization_69[0][0]     
__________________________________________________________________________________________________
conv2d_70 (Conv2D)              (None, 4, 4, 1024)   263168      activation_62[0][0]              
__________________________________________________________________________________________________
batch_normalization_70 (BatchNo (None, 4, 4, 1024)   4096        conv2d_70[0][0]                  
__________________________________________________________________________________________________
add_20 (Add)                    (None, 4, 4, 1024)   0           activation_60[0][0]              
                                                                 batch_normalization_70[0][0]     
__________________________________________________________________________________________________
activation_63 (Activation)      (None, 4, 4, 1024)   0           add_20[0][0]                     
__________________________________________________________________________________________________
conv2d_71 (Conv2D)              (None, 2, 2, 512)    524800      activation_63[0][0]              
__________________________________________________________________________________________________
batch_normalization_71 (BatchNo (None, 2, 2, 512)    2048        conv2d_71[0][0]                  
__________________________________________________________________________________________________
activation_64 (Activation)      (None, 2, 2, 512)    0           batch_normalization_71[0][0]     
__________________________________________________________________________________________________
conv2d_72 (Conv2D)              (None, 2, 2, 512)    2359808     activation_64[0][0]              
__________________________________________________________________________________________________
batch_normalization_72 (BatchNo (None, 2, 2, 512)    2048        conv2d_72[0][0]                  
__________________________________________________________________________________________________
activation_65 (Activation)      (None, 2, 2, 512)    0           batch_normalization_72[0][0]     
__________________________________________________________________________________________________
conv2d_73 (Conv2D)              (None, 2, 2, 2048)   1050624     activation_65[0][0]              
__________________________________________________________________________________________________
conv2d_74 (Conv2D)              (None, 2, 2, 2048)   2099200     activation_63[0][0]              
__________________________________________________________________________________________________
batch_normalization_73 (BatchNo (None, 2, 2, 2048)   8192        conv2d_73[0][0]                  
__________________________________________________________________________________________________
batch_normalization_74 (BatchNo (None, 2, 2, 2048)   8192        conv2d_74[0][0]                  
__________________________________________________________________________________________________
add_21 (Add)                    (None, 2, 2, 2048)   0           batch_normalization_73[0][0]     
                                                                 batch_normalization_74[0][0]     
__________________________________________________________________________________________________
activation_66 (Activation)      (None, 2, 2, 2048)   0           add_21[0][0]                     
__________________________________________________________________________________________________
conv2d_75 (Conv2D)              (None, 2, 2, 512)    1049088     activation_66[0][0]              
__________________________________________________________________________________________________
batch_normalization_75 (BatchNo (None, 2, 2, 512)    2048        conv2d_75[0][0]                  
__________________________________________________________________________________________________
activation_67 (Activation)      (None, 2, 2, 512)    0           batch_normalization_75[0][0]     
__________________________________________________________________________________________________
conv2d_76 (Conv2D)              (None, 2, 2, 512)    2359808     activation_67[0][0]              
__________________________________________________________________________________________________
batch_normalization_76 (BatchNo (None, 2, 2, 512)    2048        conv2d_76[0][0]                  
__________________________________________________________________________________________________
activation_68 (Activation)      (None, 2, 2, 512)    0           batch_normalization_76[0][0]     
__________________________________________________________________________________________________
conv2d_77 (Conv2D)              (None, 2, 2, 2048)   1050624     activation_68[0][0]              
__________________________________________________________________________________________________
batch_normalization_77 (BatchNo (None, 2, 2, 2048)   8192        conv2d_77[0][0]                  
__________________________________________________________________________________________________
add_22 (Add)                    (None, 2, 2, 2048)   0           activation_66[0][0]              
                                                                 batch_normalization_77[0][0]     
__________________________________________________________________________________________________
activation_69 (Activation)      (None, 2, 2, 2048)   0           add_22[0][0]                     
__________________________________________________________________________________________________
conv2d_78 (Conv2D)              (None, 2, 2, 512)    1049088     activation_69[0][0]              
__________________________________________________________________________________________________
batch_normalization_78 (BatchNo (None, 2, 2, 512)    2048        conv2d_78[0][0]                  
__________________________________________________________________________________________________
activation_70 (Activation)      (None, 2, 2, 512)    0           batch_normalization_78[0][0]     
__________________________________________________________________________________________________
conv2d_79 (Conv2D)              (None, 2, 2, 512)    2359808     activation_70[0][0]              
__________________________________________________________________________________________________
batch_normalization_79 (BatchNo (None, 2, 2, 512)    2048        conv2d_79[0][0]                  
__________________________________________________________________________________________________
activation_71 (Activation)      (None, 2, 2, 512)    0           batch_normalization_79[0][0]     
__________________________________________________________________________________________________
conv2d_80 (Conv2D)              (None, 2, 2, 2048)   1050624     activation_71[0][0]              
__________________________________________________________________________________________________
batch_normalization_80 (BatchNo (None, 2, 2, 2048)   8192        conv2d_80[0][0]                  
__________________________________________________________________________________________________
add_23 (Add)                    (None, 2, 2, 2048)   0           activation_69[0][0]              
                                                                 batch_normalization_80[0][0]     
__________________________________________________________________________________________________
activation_72 (Activation)      (None, 2, 2, 2048)   0           add_23[0][0]                     
__________________________________________________________________________________________________
average_pooling2d (AveragePooli (None, 1, 1, 2048)   0           activation_72[0][0]              
__________________________________________________________________________________________________
flatten (Flatten)               (None, 2048)         0           average_pooling2d[0][0]          
__________________________________________________________________________________________________
dense (Dense)                   (None, 6)            12294       flatten[0][0]                    
==================================================================================================
Total params: 23,600,006
Trainable params: 23,546,886
Non-trainable params: 53,120
__________________________________________________________________________________________________
None

from outputs import ResNet50_summary

model = ResNet50(input_shape = (64, 64, 3), classes = 6)

comparator(summary(model), ResNet50_summary)

[32mAll tests passed![0m

As shown in the Keras Tutorial Notebook, prior to 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 now is a dataset!

Let’s load your old friend, 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 10 epochs with a batch size of 32. On a GPU, it should take less than 2 minutes.

model.fit(X_train, Y_train, epochs = 10, batch_size = 32)

Epoch 1/10
34/34 [==============================] - 1s 28ms/step - loss: 2.6612 - accuracy: 0.4037
Epoch 2/10
34/34 [==============================] - 1s 23ms/step - loss: 1.2225 - accuracy: 0.7315
Epoch 3/10
34/34 [==============================] - 1s 23ms/step - loss: 0.6393 - accuracy: 0.8639
Epoch 4/10
34/34 [==============================] - 1s 23ms/step - loss: 0.7514 - accuracy: 0.8194
Epoch 5/10
34/34 [==============================] - 1s 23ms/step - loss: 0.4418 - accuracy: 0.8657
Epoch 6/10
34/34 [==============================] - 1s 23ms/step - loss: 0.3820 - accuracy: 0.9037
Epoch 7/10
34/34 [==============================] - 1s 23ms/step - loss: 0.2939 - accuracy: 0.9157
Epoch 8/10
34/34 [==============================] - 1s 23ms/step - loss: 0.1382 - accuracy: 0.9602
Epoch 9/10
34/34 [==============================] - 1s 23ms/step - loss: 0.1968 - accuracy: 0.9380
Epoch 10/10
34/34 [==============================] - 1s 23ms/step - loss: 0.4046 - accuracy: 0.9046





<tensorflow.python.keras.callbacks.History at 0x7f7b2869e1d0>

Expected Output:

Epoch 1/10
34/34 [==============================] - 1s 34ms/step - loss: 1.9241 - accuracy: 0.4620
Epoch 2/10
34/34 [==============================] - 2s 57ms/step - loss: 0.6403 - accuracy: 0.7898
Epoch 3/10
34/34 [==============================] - 1s 24ms/step - loss: 0.3744 - accuracy: 0.8731
Epoch 4/10
34/34 [==============================] - 2s 44ms/step - loss: 0.2220 - accuracy: 0.9231
Epoch 5/10
34/34 [==============================] - 2s 57ms/step - loss: 0.1333 - accuracy: 0.9583
Epoch 6/10
34/34 [==============================] - 2s 52ms/step - loss: 0.2243 - accuracy: 0.9444
Epoch 7/10
34/34 [==============================] - 2s 48ms/step - loss: 0.2913 - accuracy: 0.9102
Epoch 8/10
34/34 [==============================] - 1s 30ms/step - loss: 0.2269 - accuracy: 0.9306
Epoch 9/10
34/34 [==============================] - 2s 46ms/step - loss: 0.1113 - accuracy: 0.9630
Epoch 10/10
34/34 [==============================] - 2s 57ms/step - loss: 0.0709 - accuracy: 0.9778

The exact values could not match, but don’t worry about that. The important thing that you must see is that the loss value decreases, and the accuracy increases for the firsts 5 epochs.

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]))

4/4 [==============================] - 0s 8ms/step - loss: 0.2190 - accuracy: 0.9333
Loss = 0.2189994752407074
Test Accuracy = 0.9333333373069763

Expected Output:

Test Accuracy

>0.80

For the purposes of this assignment, you’ve been asked to train the model for ten epochs. You can see that it performs well. The online grader will only run your code for a small number of epochs as well. Please go ahead and submit your assignment.

After you have finished this official (graded) part of this assignment, you can also optionally train the ResNet for more iterations, if you want. It tends to get much better performance when trained for ~20 epochs, but this does take more than an hour when training on a CPU.

Using a GPU, this ResNet50 model’s weights were trained on the SIGNS dataset. You can load and run the trained model on the test set in the cells below. It may take ≈1min to load the model. Have fun!

pre_trained_model = tf.keras.models.load_model('resnet50.h5')

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

4/4 [==============================] - 0s 7ms/step - loss: 0.1596 - accuracy: 0.9500
Loss = 0.15958689153194427
Test Accuracy = 0.949999988079071

Congratulations on finishing this assignment! You’ve now implemented a state-of-the-art image classification system! Woo hoo!

ResNet50 is a powerful model for image classification when it’s trained for an adequate number of iterations. Hopefully, from this point, you can use what you’ve learned and apply it to your own classification problem to perform state-of-the-art accuracy.

What you should remember:

Very deep “plain” networks don’t work in practice because vanishing gradients make them hard to train.
Skip connections help address the Vanishing Gradient problem. They also make it easy for a ResNet block to learn an identity function.
There are two main types of blocks: The identity block and the convolutional block.
Very deep Residual Networks are built by stacking these blocks together.

Free Up Resources for Other Learners

If you don’t plan on continuing to the next Optional section, help us to provide our learners a smooth learning experience, by freeing up the resources used by your assignment by running the cell below so that the other learners can take advantage of those resources just as much as you did. Thank you!

Note:

Run the cell below when you are done with the assignment and are ready to submit it for grading.
When you’ll run it, a pop up will open, click Ok.
Running the cell will restart the kernel.

%%javascript
IPython.notebook.save_checkpoint();
if (confirm("Clear memory?") == true)
{
    IPython.notebook.kernel.restart();
}

5 - 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)
imshow(img)
prediction = pre_trained_model.predict(x)
print("Class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = ", prediction)
print("Class:", np.argmax(prediction))

Input image shape: (1, 64, 64, 3)
Class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] =  [[9.2633512e-05 4.2629417e-02 9.2548847e-01 4.1606426e-04 3.1337839e-02
  3.5633519e-05]]
Class: 2

png

Even though the model has high accuracy, it might be performing poorly on your own set of images. Notice that, the shape of the pictures, the lighting where the photos were taken, and all of the preprocessing steps can have an impact on the performance of the model. Considering everything you have learned in this specialization so far, what do you think might be the cause here?

Hint: It might be related to some distributions. Can you come up with a potential solution ?

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

pre_trained_model.summary()

Model: "ResNet50"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 64, 64, 3)]  0                                            
__________________________________________________________________________________________________
zero_padding2d (ZeroPadding2D)  (None, 70, 70, 3)    0           input_1[0][0]                    
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, 32, 32, 64)   9472        zero_padding2d[0][0]             
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization)   (None, 32, 32, 64)   256         conv2d_7[0][0]                   
__________________________________________________________________________________________________
activation_6 (Activation)       (None, 32, 32, 64)   0           bn_conv1[0][0]                   
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D)    (None, 15, 15, 64)   0           activation_6[0][0]               
__________________________________________________________________________________________________
conv2d_8 (Conv2D)               (None, 15, 15, 64)   4160        max_pooling2d[0][0]              
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 15, 15, 64)   256         conv2d_8[0][0]                   
__________________________________________________________________________________________________
activation_7 (Activation)       (None, 15, 15, 64)   0           batch_normalization_7[0][0]      
__________________________________________________________________________________________________
conv2d_9 (Conv2D)               (None, 15, 15, 64)   36928       activation_7[0][0]               
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 15, 15, 64)   256         conv2d_9[0][0]                   
__________________________________________________________________________________________________
activation_8 (Activation)       (None, 15, 15, 64)   0           batch_normalization_8[0][0]      
__________________________________________________________________________________________________
conv2d_10 (Conv2D)              (None, 15, 15, 256)  16640       activation_8[0][0]               
__________________________________________________________________________________________________
conv2d_11 (Conv2D)              (None, 15, 15, 256)  16640       max_pooling2d[0][0]              
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 15, 15, 256)  1024        conv2d_10[0][0]                  
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 15, 15, 256)  1024        conv2d_11[0][0]                  
__________________________________________________________________________________________________
add_2 (Add)                     (None, 15, 15, 256)  0           batch_normalization_9[0][0]      
                                                                 batch_normalization_10[0][0]     
__________________________________________________________________________________________________
activation_9 (Activation)       (None, 15, 15, 256)  0           add_2[0][0]                      
__________________________________________________________________________________________________
conv2d_12 (Conv2D)              (None, 15, 15, 64)   16448       activation_9[0][0]               
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 15, 15, 64)   256         conv2d_12[0][0]                  
__________________________________________________________________________________________________
activation_10 (Activation)      (None, 15, 15, 64)   0           batch_normalization_11[0][0]     
__________________________________________________________________________________________________
conv2d_13 (Conv2D)              (None, 15, 15, 64)   36928       activation_10[0][0]              
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 15, 15, 64)   256         conv2d_13[0][0]                  
__________________________________________________________________________________________________
activation_11 (Activation)      (None, 15, 15, 64)   0           batch_normalization_12[0][0]     
__________________________________________________________________________________________________
conv2d_14 (Conv2D)              (None, 15, 15, 256)  16640       activation_11[0][0]              
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 15, 15, 256)  1024        conv2d_14[0][0]                  
__________________________________________________________________________________________________
add_3 (Add)                     (None, 15, 15, 256)  0           batch_normalization_13[0][0]     
                                                                 activation_9[0][0]               
__________________________________________________________________________________________________
activation_12 (Activation)      (None, 15, 15, 256)  0           add_3[0][0]                      
__________________________________________________________________________________________________
conv2d_15 (Conv2D)              (None, 15, 15, 64)   16448       activation_12[0][0]              
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 15, 15, 64)   256         conv2d_15[0][0]                  
__________________________________________________________________________________________________
activation_13 (Activation)      (None, 15, 15, 64)   0           batch_normalization_14[0][0]     
__________________________________________________________________________________________________
conv2d_16 (Conv2D)              (None, 15, 15, 64)   36928       activation_13[0][0]              
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 15, 15, 64)   256         conv2d_16[0][0]                  
__________________________________________________________________________________________________
activation_14 (Activation)      (None, 15, 15, 64)   0           batch_normalization_15[0][0]     
__________________________________________________________________________________________________
conv2d_17 (Conv2D)              (None, 15, 15, 256)  16640       activation_14[0][0]              
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 15, 15, 256)  1024        conv2d_17[0][0]                  
__________________________________________________________________________________________________
add_4 (Add)                     (None, 15, 15, 256)  0           batch_normalization_16[0][0]     
                                                                 activation_12[0][0]              
__________________________________________________________________________________________________
activation_15 (Activation)      (None, 15, 15, 256)  0           add_4[0][0]                      
__________________________________________________________________________________________________
conv2d_18 (Conv2D)              (None, 8, 8, 128)    32896       activation_15[0][0]              
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 8, 8, 128)    512         conv2d_18[0][0]                  
__________________________________________________________________________________________________
activation_16 (Activation)      (None, 8, 8, 128)    0           batch_normalization_17[0][0]     
__________________________________________________________________________________________________
conv2d_19 (Conv2D)              (None, 8, 8, 128)    147584      activation_16[0][0]              
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 8, 8, 128)    512         conv2d_19[0][0]                  
__________________________________________________________________________________________________
activation_17 (Activation)      (None, 8, 8, 128)    0           batch_normalization_18[0][0]     
__________________________________________________________________________________________________
conv2d_20 (Conv2D)              (None, 8, 8, 512)    66048       activation_17[0][0]              
__________________________________________________________________________________________________
conv2d_21 (Conv2D)              (None, 8, 8, 512)    131584      activation_15[0][0]              
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 8, 8, 512)    2048        conv2d_20[0][0]                  
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 8, 8, 512)    2048        conv2d_21[0][0]                  
__________________________________________________________________________________________________
add_5 (Add)                     (None, 8, 8, 512)    0           batch_normalization_19[0][0]     
                                                                 batch_normalization_20[0][0]     
__________________________________________________________________________________________________
activation_18 (Activation)      (None, 8, 8, 512)    0           add_5[0][0]                      
__________________________________________________________________________________________________
conv2d_22 (Conv2D)              (None, 8, 8, 128)    65664       activation_18[0][0]              
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 8, 8, 128)    512         conv2d_22[0][0]                  
__________________________________________________________________________________________________
activation_19 (Activation)      (None, 8, 8, 128)    0           batch_normalization_21[0][0]     
__________________________________________________________________________________________________
conv2d_23 (Conv2D)              (None, 8, 8, 128)    147584      activation_19[0][0]              
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 8, 8, 128)    512         conv2d_23[0][0]                  
__________________________________________________________________________________________________
activation_20 (Activation)      (None, 8, 8, 128)    0           batch_normalization_22[0][0]     
__________________________________________________________________________________________________
conv2d_24 (Conv2D)              (None, 8, 8, 512)    66048       activation_20[0][0]              
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 8, 8, 512)    2048        conv2d_24[0][0]                  
__________________________________________________________________________________________________
add_6 (Add)                     (None, 8, 8, 512)    0           batch_normalization_23[0][0]     
                                                                 activation_18[0][0]              
__________________________________________________________________________________________________
activation_21 (Activation)      (None, 8, 8, 512)    0           add_6[0][0]                      
__________________________________________________________________________________________________
conv2d_25 (Conv2D)              (None, 8, 8, 128)    65664       activation_21[0][0]              
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 8, 8, 128)    512         conv2d_25[0][0]                  
__________________________________________________________________________________________________
activation_22 (Activation)      (None, 8, 8, 128)    0           batch_normalization_24[0][0]     
__________________________________________________________________________________________________
conv2d_26 (Conv2D)              (None, 8, 8, 128)    147584      activation_22[0][0]              
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 8, 8, 128)    512         conv2d_26[0][0]                  
__________________________________________________________________________________________________
activation_23 (Activation)      (None, 8, 8, 128)    0           batch_normalization_25[0][0]     
__________________________________________________________________________________________________
conv2d_27 (Conv2D)              (None, 8, 8, 512)    66048       activation_23[0][0]              
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, 8, 8, 512)    2048        conv2d_27[0][0]                  
__________________________________________________________________________________________________
add_7 (Add)                     (None, 8, 8, 512)    0           batch_normalization_26[0][0]     
                                                                 activation_21[0][0]              
__________________________________________________________________________________________________
activation_24 (Activation)      (None, 8, 8, 512)    0           add_7[0][0]                      
__________________________________________________________________________________________________
conv2d_28 (Conv2D)              (None, 8, 8, 128)    65664       activation_24[0][0]              
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, 8, 8, 128)    512         conv2d_28[0][0]                  
__________________________________________________________________________________________________
activation_25 (Activation)      (None, 8, 8, 128)    0           batch_normalization_27[0][0]     
__________________________________________________________________________________________________
conv2d_29 (Conv2D)              (None, 8, 8, 128)    147584      activation_25[0][0]              
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, 8, 8, 128)    512         conv2d_29[0][0]                  
__________________________________________________________________________________________________
activation_26 (Activation)      (None, 8, 8, 128)    0           batch_normalization_28[0][0]     
__________________________________________________________________________________________________
conv2d_30 (Conv2D)              (None, 8, 8, 512)    66048       activation_26[0][0]              
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, 8, 8, 512)    2048        conv2d_30[0][0]                  
__________________________________________________________________________________________________
add_8 (Add)                     (None, 8, 8, 512)    0           batch_normalization_29[0][0]     
                                                                 activation_24[0][0]              
__________________________________________________________________________________________________
activation_27 (Activation)      (None, 8, 8, 512)    0           add_8[0][0]                      
__________________________________________________________________________________________________
conv2d_31 (Conv2D)              (None, 4, 4, 256)    131328      activation_27[0][0]              
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, 4, 4, 256)    1024        conv2d_31[0][0]                  
__________________________________________________________________________________________________
activation_28 (Activation)      (None, 4, 4, 256)    0           batch_normalization_30[0][0]     
__________________________________________________________________________________________________
conv2d_32 (Conv2D)              (None, 4, 4, 256)    590080      activation_28[0][0]              
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, 4, 4, 256)    1024        conv2d_32[0][0]                  
__________________________________________________________________________________________________
activation_29 (Activation)      (None, 4, 4, 256)    0           batch_normalization_31[0][0]     
__________________________________________________________________________________________________
conv2d_33 (Conv2D)              (None, 4, 4, 1024)   263168      activation_29[0][0]              
__________________________________________________________________________________________________
conv2d_34 (Conv2D)              (None, 4, 4, 1024)   525312      activation_27[0][0]              
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, 4, 4, 1024)   4096        conv2d_33[0][0]                  
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, 4, 4, 1024)   4096        conv2d_34[0][0]                  
__________________________________________________________________________________________________
add_9 (Add)                     (None, 4, 4, 1024)   0           batch_normalization_32[0][0]     
                                                                 batch_normalization_33[0][0]     
__________________________________________________________________________________________________
activation_30 (Activation)      (None, 4, 4, 1024)   0           add_9[0][0]                      
__________________________________________________________________________________________________
conv2d_35 (Conv2D)              (None, 4, 4, 256)    262400      activation_30[0][0]              
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, 4, 4, 256)    1024        conv2d_35[0][0]                  
__________________________________________________________________________________________________
activation_31 (Activation)      (None, 4, 4, 256)    0           batch_normalization_34[0][0]     
__________________________________________________________________________________________________
conv2d_36 (Conv2D)              (None, 4, 4, 256)    590080      activation_31[0][0]              
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, 4, 4, 256)    1024        conv2d_36[0][0]                  
__________________________________________________________________________________________________
activation_32 (Activation)      (None, 4, 4, 256)    0           batch_normalization_35[0][0]     
__________________________________________________________________________________________________
conv2d_37 (Conv2D)              (None, 4, 4, 1024)   263168      activation_32[0][0]              
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, 4, 4, 1024)   4096        conv2d_37[0][0]                  
__________________________________________________________________________________________________
add_10 (Add)                    (None, 4, 4, 1024)   0           batch_normalization_36[0][0]     
                                                                 activation_30[0][0]              
__________________________________________________________________________________________________
activation_33 (Activation)      (None, 4, 4, 1024)   0           add_10[0][0]                     
__________________________________________________________________________________________________
conv2d_38 (Conv2D)              (None, 4, 4, 256)    262400      activation_33[0][0]              
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, 4, 4, 256)    1024        conv2d_38[0][0]                  
__________________________________________________________________________________________________
activation_34 (Activation)      (None, 4, 4, 256)    0           batch_normalization_37[0][0]     
__________________________________________________________________________________________________
conv2d_39 (Conv2D)              (None, 4, 4, 256)    590080      activation_34[0][0]              
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, 4, 4, 256)    1024        conv2d_39[0][0]                  
__________________________________________________________________________________________________
activation_35 (Activation)      (None, 4, 4, 256)    0           batch_normalization_38[0][0]     
__________________________________________________________________________________________________
conv2d_40 (Conv2D)              (None, 4, 4, 1024)   263168      activation_35[0][0]              
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, 4, 4, 1024)   4096        conv2d_40[0][0]                  
__________________________________________________________________________________________________
add_11 (Add)                    (None, 4, 4, 1024)   0           batch_normalization_39[0][0]     
                                                                 activation_33[0][0]              
__________________________________________________________________________________________________
activation_36 (Activation)      (None, 4, 4, 1024)   0           add_11[0][0]                     
__________________________________________________________________________________________________
conv2d_41 (Conv2D)              (None, 4, 4, 256)    262400      activation_36[0][0]              
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, 4, 4, 256)    1024        conv2d_41[0][0]                  
__________________________________________________________________________________________________
activation_37 (Activation)      (None, 4, 4, 256)    0           batch_normalization_40[0][0]     
__________________________________________________________________________________________________
conv2d_42 (Conv2D)              (None, 4, 4, 256)    590080      activation_37[0][0]              
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, 4, 4, 256)    1024        conv2d_42[0][0]                  
__________________________________________________________________________________________________
activation_38 (Activation)      (None, 4, 4, 256)    0           batch_normalization_41[0][0]     
__________________________________________________________________________________________________
conv2d_43 (Conv2D)              (None, 4, 4, 1024)   263168      activation_38[0][0]              
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, 4, 4, 1024)   4096        conv2d_43[0][0]                  
__________________________________________________________________________________________________
add_12 (Add)                    (None, 4, 4, 1024)   0           batch_normalization_42[0][0]     
                                                                 activation_36[0][0]              
__________________________________________________________________________________________________
activation_39 (Activation)      (None, 4, 4, 1024)   0           add_12[0][0]                     
__________________________________________________________________________________________________
conv2d_44 (Conv2D)              (None, 4, 4, 256)    262400      activation_39[0][0]              
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, 4, 4, 256)    1024        conv2d_44[0][0]                  
__________________________________________________________________________________________________
activation_40 (Activation)      (None, 4, 4, 256)    0           batch_normalization_43[0][0]     
__________________________________________________________________________________________________
conv2d_45 (Conv2D)              (None, 4, 4, 256)    590080      activation_40[0][0]              
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, 4, 4, 256)    1024        conv2d_45[0][0]                  
__________________________________________________________________________________________________
activation_41 (Activation)      (None, 4, 4, 256)    0           batch_normalization_44[0][0]     
__________________________________________________________________________________________________
conv2d_46 (Conv2D)              (None, 4, 4, 1024)   263168      activation_41[0][0]              
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, 4, 4, 1024)   4096        conv2d_46[0][0]                  
__________________________________________________________________________________________________
add_13 (Add)                    (None, 4, 4, 1024)   0           batch_normalization_45[0][0]     
                                                                 activation_39[0][0]              
__________________________________________________________________________________________________
activation_42 (Activation)      (None, 4, 4, 1024)   0           add_13[0][0]                     
__________________________________________________________________________________________________
conv2d_47 (Conv2D)              (None, 4, 4, 256)    262400      activation_42[0][0]              
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, 4, 4, 256)    1024        conv2d_47[0][0]                  
__________________________________________________________________________________________________
activation_43 (Activation)      (None, 4, 4, 256)    0           batch_normalization_46[0][0]     
__________________________________________________________________________________________________
conv2d_48 (Conv2D)              (None, 4, 4, 256)    590080      activation_43[0][0]              
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, 4, 4, 256)    1024        conv2d_48[0][0]                  
__________________________________________________________________________________________________
activation_44 (Activation)      (None, 4, 4, 256)    0           batch_normalization_47[0][0]     
__________________________________________________________________________________________________
conv2d_49 (Conv2D)              (None, 4, 4, 1024)   263168      activation_44[0][0]              
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, 4, 4, 1024)   4096        conv2d_49[0][0]                  
__________________________________________________________________________________________________
add_14 (Add)                    (None, 4, 4, 1024)   0           batch_normalization_48[0][0]     
                                                                 activation_42[0][0]              
__________________________________________________________________________________________________
activation_45 (Activation)      (None, 4, 4, 1024)   0           add_14[0][0]                     
__________________________________________________________________________________________________
conv2d_50 (Conv2D)              (None, 2, 2, 512)    524800      activation_45[0][0]              
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, 2, 2, 512)    2048        conv2d_50[0][0]                  
__________________________________________________________________________________________________
activation_46 (Activation)      (None, 2, 2, 512)    0           batch_normalization_49[0][0]     
__________________________________________________________________________________________________
conv2d_51 (Conv2D)              (None, 2, 2, 512)    2359808     activation_46[0][0]              
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, 2, 2, 512)    2048        conv2d_51[0][0]                  
__________________________________________________________________________________________________
activation_47 (Activation)      (None, 2, 2, 512)    0           batch_normalization_50[0][0]     
__________________________________________________________________________________________________
conv2d_52 (Conv2D)              (None, 2, 2, 2048)   1050624     activation_47[0][0]              
__________________________________________________________________________________________________
conv2d_53 (Conv2D)              (None, 2, 2, 2048)   2099200     activation_45[0][0]              
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, 2, 2, 2048)   8192        conv2d_52[0][0]                  
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, 2, 2, 2048)   8192        conv2d_53[0][0]                  
__________________________________________________________________________________________________
add_15 (Add)                    (None, 2, 2, 2048)   0           batch_normalization_51[0][0]     
                                                                 batch_normalization_52[0][0]     
__________________________________________________________________________________________________
activation_48 (Activation)      (None, 2, 2, 2048)   0           add_15[0][0]                     
__________________________________________________________________________________________________
conv2d_54 (Conv2D)              (None, 2, 2, 512)    1049088     activation_48[0][0]              
__________________________________________________________________________________________________
batch_normalization_53 (BatchNo (None, 2, 2, 512)    2048        conv2d_54[0][0]                  
__________________________________________________________________________________________________
activation_49 (Activation)      (None, 2, 2, 512)    0           batch_normalization_53[0][0]     
__________________________________________________________________________________________________
conv2d_55 (Conv2D)              (None, 2, 2, 512)    2359808     activation_49[0][0]              
__________________________________________________________________________________________________
batch_normalization_54 (BatchNo (None, 2, 2, 512)    2048        conv2d_55[0][0]                  
__________________________________________________________________________________________________
activation_50 (Activation)      (None, 2, 2, 512)    0           batch_normalization_54[0][0]     
__________________________________________________________________________________________________
conv2d_56 (Conv2D)              (None, 2, 2, 2048)   1050624     activation_50[0][0]              
__________________________________________________________________________________________________
batch_normalization_55 (BatchNo (None, 2, 2, 2048)   8192        conv2d_56[0][0]                  
__________________________________________________________________________________________________
add_16 (Add)                    (None, 2, 2, 2048)   0           batch_normalization_55[0][0]     
                                                                 activation_48[0][0]              
__________________________________________________________________________________________________
activation_51 (Activation)      (None, 2, 2, 2048)   0           add_16[0][0]                     
__________________________________________________________________________________________________
conv2d_57 (Conv2D)              (None, 2, 2, 512)    1049088     activation_51[0][0]              
__________________________________________________________________________________________________
batch_normalization_56 (BatchNo (None, 2, 2, 512)    2048        conv2d_57[0][0]                  
__________________________________________________________________________________________________
activation_52 (Activation)      (None, 2, 2, 512)    0           batch_normalization_56[0][0]     
__________________________________________________________________________________________________
conv2d_58 (Conv2D)              (None, 2, 2, 512)    2359808     activation_52[0][0]              
__________________________________________________________________________________________________
batch_normalization_57 (BatchNo (None, 2, 2, 512)    2048        conv2d_58[0][0]                  
__________________________________________________________________________________________________
activation_53 (Activation)      (None, 2, 2, 512)    0           batch_normalization_57[0][0]     
__________________________________________________________________________________________________
conv2d_59 (Conv2D)              (None, 2, 2, 2048)   1050624     activation_53[0][0]              
__________________________________________________________________________________________________
batch_normalization_58 (BatchNo (None, 2, 2, 2048)   8192        conv2d_59[0][0]                  
__________________________________________________________________________________________________
add_17 (Add)                    (None, 2, 2, 2048)   0           batch_normalization_58[0][0]     
                                                                 activation_51[0][0]              
__________________________________________________________________________________________________
activation_54 (Activation)      (None, 2, 2, 2048)   0           add_17[0][0]                     
__________________________________________________________________________________________________
average_pooling2d (AveragePooli (None, 1, 1, 2048)   0           activation_54[0][0]              
__________________________________________________________________________________________________
flatten (Flatten)               (None, 2048)         0           average_pooling2d[0][0]          
__________________________________________________________________________________________________
fc6 (Dense)                     (None, 6)            12294       flatten[0][0]                    
==================================================================================================
Total params: 23,600,006
Trainable params: 23,546,886
Non-trainable params: 53,120
__________________________________________________________________________________________________

Free Up Resources for Other Learners

In order to provide our learners a smooth learning experience, please free up the resources used by your assignment by running the cell below so that the other learners can take advantage of those resources just as much as you did. Thank you!

Note:

Run the cell below when you are done with the assignment and are ready to submit it for grading.
When you’ll run it, a pop up will open, click Ok.
Running the cell will restart the kernel.

%%javascript
IPython.notebook.save_checkpoint();
if (confirm("Clear memory?") == true)
{
    IPython.notebook.kernel.restart();
}

6 - Bibliography

This notebook presents the ResNet algorithm from He et al. (2015). The implementation here also took significant inspiration and follows the structure given in the GitHub repository of Francois Chollet:

Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun - Deep Residual Learning for Image Recognition (2015)
Francois Chollet’s GitHub repository: https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py