As in this, the author is using CNN to extract features of the images, and then doing SVM for further analysis. My question is how to extract features in CNN?

E.g., here is a CNN code I'm using:

%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from sklearn.metrics import confusion_matrix
import time
from datetime import timedelta
import math
# Convolutional Layer 1.
filter_size1 = 5          # Convolution filters are 5 x 5 pixels.
num_filters1 = 16         # There are 16 of these filters.
# Convolutional Layer 2.
filter_size2 = 5          # Convolution filters are 5 x 5 pixels.
num_filters2 = 36         # There are 36 of these filters.
# Fully-connected layer.
fc_size = 128             # Number of neurons in fully-connected layer.
from tensorflow.examples.tutorials.mnist import input_data
data = input_data.read_data_sets('data/MNIST/', one_hot=True)
data.test.cls = np.argmax(data.test.labels, axis=1)
# We know that MNIST images are 28 pixels in each dimension.
img_size = 28
# Images are stored in one-dimensional arrays of this length.
img_size_flat = img_size * img_size
# Tuple with height and width of images used to reshape arrays.
img_shape = (img_size, img_size)
# Number of colour channels for the images: 1 channel for gray-scale.
num_channels = 1
# Number of classes, one class for each of 10 digits.
num_classes = 10
def new_weights(shape):
    return tf.Variable(tf.truncated_normal(shape, stddev=0.05))
def new_biases(length):
    return tf.Variable(tf.constant(0.05, shape=[length]))
def new_conv_layer(input,              # The previous layer.
                   num_input_channels, # Num. channels in prev. layer.
                   filter_size,        # Width and height of each filter.
                   num_filters,        # Number of filters.
                   use_pooling=True):  # Use 2x2 max-pooling.
    # Shape of the filter-weights for the convolution.
    # This format is determined by the TensorFlow API.
    shape = [filter_size, filter_size, num_input_channels, num_filters]
    # Create new weights aka. filters with the given shape.
    weights = new_weights(shape=shape)
    # Create new biases, one for each filter.
    biases = new_biases(length=num_filters)
    # Create the TensorFlow operation for convolution.
    # Note the strides are set to 1 in all dimensions.
    # The first and last stride must always be 1,
    # because the first is for the image-number and
    # the last is for the input-channel.
    # But e.g. strides=[1, 2, 2, 1] would mean that the filter
    # is moved 2 pixels across the x- and y-axis of the image.
    # The padding is set to 'SAME' which means the input image
    # is padded with zeroes so the size of the output is the same.
    layer = tf.nn.conv2d(input=input,
                         strides=[1, 1, 1, 1],
    # Add the biases to the results of the convolution.
    # A bias-value is added to each filter-channel.
    layer += biases
    # Use pooling to down-sample the image resolution?
    if use_pooling:
        # This is 2x2 max-pooling, which means that we
        # consider 2x2 windows and select the largest value
        # in each window. Then we move 2 pixels to the next window.
        layer = tf.nn.max_pool(value=layer,
                               ksize=[1, 2, 2, 1],
                               strides=[1, 2, 2, 1],
    # Rectified Linear Unit (ReLU).
    # It calculates max(x, 0) for each input pixel x.
    # This adds some non-linearity to the formula and allows us
    # to learn more complicated functions.
    layer = tf.nn.relu(layer)
    # Note that ReLU is normally executed before the pooling,
    # but since relu(max_pool(x)) == max_pool(relu(x)) we can
    # save 75% of the relu-operations by max-pooling first.
    # We return both the resulting layer and the filter-weights
    # because we will plot the weights later.
    return layer, weights
def flatten_layer(layer):
    # Get the shape of the input layer.
    layer_shape = layer.get_shape()
    # The shape of the input layer is assumed to be:
    # layer_shape == [num_images, img_height, img_width, num_channels]
    # The number of features is: img_height * img_width * num_channels
    # We can use a function from TensorFlow to calculate this.
    num_features = layer_shape[1:4].num_elements()
    # Reshape the layer to [num_images, num_features].
    # Note that we just set the size of the second dimension
    # to num_features and the size of the first dimension to -1
    # which means the size in that dimension is calculated
    # so the total size of the tensor is unchanged from the reshaping.
    layer_flat = tf.reshape(layer, [-1, num_features])
    # The shape of the flattened layer is now:
    # [num_images, img_height * img_width * num_channels]
    # Return both the flattened layer and the number of features.
    return layer_flat, num_features
def new_fc_layer(input,          # The previous layer.
                 num_inputs,     # Num. inputs from prev. layer.
                 num_outputs,    # Num. outputs.
                 use_relu=True): # Use Rectified Linear Unit (ReLU)?
    # Create new weights and biases.
    weights = new_weights(shape=[num_inputs, num_outputs])
    biases = new_biases(length=num_outputs)
    # Calculate the layer as the matrix multiplication of
    # the input and weights, and then add the bias-values.
    layer = tf.matmul(input, weights) + biases
    # Use ReLU?
    if use_relu:
        layer = tf.nn.relu(layer)
    return layer
x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')
x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])
y_true = tf.placeholder(tf.float32, shape=[None, 10], name='y_true')
y_true_cls = tf.argmax(y_true, dimension=1)
layer_conv1, weights_conv1 = \
layer_conv2, weights_conv2 = \
layer_fc1 = new_fc_layer(input=layer_flat,
layer_fc2 = new_fc_layer(input=layer_fc1,
y_pred = tf.nn.softmax(layer_fc2)
y_pred_cls = tf.argmax(y_pred, dimension=1)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2,
cost = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost)
correct_prediction = tf.equal(y_pred_cls, y_true_cls)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
session = tf.Session()
train_batch_size = 64
# Counter for total number of iterations performed so far.
total_iterations = 0
def optimize(num_iterations):
    # Ensure we update the global variable rather than a local copy.
    global total_iterations
    # Start-time used for printing time-usage below.
    start_time = time.time()
    for i in range(total_iterations,
                   total_iterations + num_iterations):
        # Get a batch of training examples.
        # x_batch now holds a batch of images and
        # y_true_batch are the true labels for those images.
        x_batch, y_true_batch = data.train.next_batch(train_batch_size)
        # Put the batch into a dict with the proper names
        # for placeholder variables in the TensorFlow graph.
        feed_dict_train = {x: x_batch,
                           y_true: y_true_batch}
        # Run the optimizer using this batch of training data.
        # TensorFlow assigns the variables in feed_dict_train
        # to the placeholder variables and then runs the optimizer.
        session.run(optimizer, feed_dict=feed_dict_train)
        # Print status every 100 iterations.
        if i % 100 == 0:
            # Calculate the accuracy on the training-set.
            acc = session.run(accuracy, feed_dict=feed_dict_train)
            # Message for printing.
            msg = "Optimization Iteration: {0:>6}, Training Accuracy: {1:>6.1%}"
            # Print it.
            print(msg.format(i + 1, acc))
    # Update the total number of iterations performed.
    total_iterations += num_iterations
    # Ending time.
    end_time = time.time()
    # Difference between start and end-times.
    time_dif = end_time - start_time
    # Print the time-usage.
    print("Time usage: " + str(timedelta(seconds=int(round(time_dif)))))


In this case, how to extract the features? Also, I want to know that are the extracted features different filters that we used or the final updated weights that we get after completion of CNN?

  • $\begingroup$ You need to define your network first. Then you should take the output of the last layer of your network. That's a feature $\endgroup$
    – enterML
    May 13 '17 at 17:06
  • $\begingroup$ @Nain: Thanks for your answer! Can you please put your comment in the answer section, so that I can marked it correct after testing it. Thank you! $\endgroup$
    – Beta
    May 13 '17 at 17:08

Before trying to extract features, you need to define your network. Suppose your network has an architecture like this:

Conv1 layer
Conv2 layer
Conv3 layer
Dense1 layer
Dense2 layer 

Now you can extract features for each input for any layer (say for Conv2) in the following way:

conv2_tensor = sess.graph.get_tensor_by_name('Conv2')
_, conv_val = sess.run([conv2_tensor],
                                  {'x': image_data})

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