docs/vision/wrn.html

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<article id="content">
<header>
<h1 class="title">Module <code>ktrain.vision.wrn</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from ..imports import *

weight_decay = 0.0005


def initial_conv(input):
    x = keras.layers.Convolution2D(
        16,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(input)

    channel_axis = 1 if K.image_data_format() == &#34;channels_first&#34; else -1

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)
    return x


def expand_conv(init, base, k, strides=(1, 1)):
    x = keras.layers.Convolution2D(
        base * k,
        (3, 3),
        padding=&#34;same&#34;,
        strides=strides,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(init)

    channel_axis = 1 if K.image_data_format() == &#34;channels_first&#34; else -1

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)

    x = keras.layers.Convolution2D(
        base * k,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(x)

    skip = keras.layers.Convolution2D(
        base * k,
        (1, 1),
        padding=&#34;same&#34;,
        strides=strides,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(init)

    m = keras.layers.Add()([x, skip])

    return m


def conv1_block(input, k=1, dropout=0.0):
    init = input

    channel_axis = 1 if K.image_data_format() == &#34;channels_first&#34; else -1

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(input)
    x = keras.layers.Activation(&#34;relu&#34;)(x)
    x = keras.layers.Convolution2D(
        16 * k,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(x)

    if dropout &gt; 0.0:
        x = keras.layers.Dropout(dropout)(x)

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)
    x = keras.layers.Convolution2D(
        16 * k,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(x)

    m = keras.layers.Add()([init, x])
    return m


def conv2_block(input, k=1, dropout=0.0):
    init = input

    # channel_axis = 1 if K.image_dim_ordering() == &#34;th&#34; else -1
    channel_axis = -1

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(input)
    x = keras.layers.Activation(&#34;relu&#34;)(x)
    x = keras.layers.Convolution2D(
        32 * k,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(x)

    if dropout &gt; 0.0:
        x = keras.layers.Dropout(dropout)(x)

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)
    x = keras.layers.Convolution2D(
        32 * k,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(x)

    m = keras.layers.Add()([init, x])
    return m


def conv3_block(input, k=1, dropout=0.0):
    init = input

    # channel_axis = 1 if K.image_dim_ordering() == &#34;th&#34; else -1
    channel_axis = -1

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(input)
    x = keras.layers.Activation(&#34;relu&#34;)(x)
    x = keras.layers.Convolution2D(
        64 * k,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(x)

    if dropout &gt; 0.0:
        x = keras.layers.Dropout(dropout)(x)

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)
    x = keras.layers.Convolution2D(
        64 * k,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(x)

    m = keras.layers.Add()([init, x])
    return m


def create_wide_residual_network(
    input_dim, nb_classes=100, N=2, k=1, activation=&#34;softmax&#34;, dropout=0.0, verbose=1
):
    &#34;&#34;&#34;
    Creates a Wide Residual Network with specified parameters

    :param input: Input Keras object
    :param nb_classes: Number of output classes
    :param N: Depth of the network. Compute N = (n - 4) / 6.
              Example : For a depth of 16, n = 16, N = (16 - 4) / 6 = 2
              Example2: For a depth of 28, n = 28, N = (28 - 4) / 6 = 4
              Example3: For a depth of 40, n = 40, N = (40 - 4) / 6 = 6
    :param k: Width of the network.
    :param dropout: Adds dropout if value is greater than 0.0
    :param verbose: Debug info to describe created WRN
    :return:
    &#34;&#34;&#34;
    channel_axis = 1 if K.image_data_format() == &#34;channels_first&#34; else -1

    ip = keras.layers.Input(shape=input_dim)

    x = initial_conv(ip)
    nb_conv = 4

    x = expand_conv(x, 16, k)
    nb_conv += 2

    for i in range(N - 1):
        x = conv1_block(x, k, dropout)
        nb_conv += 2

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)

    x = expand_conv(x, 32, k, strides=(2, 2))
    nb_conv += 2

    for i in range(N - 1):
        x = conv2_block(x, k, dropout)
        nb_conv += 2

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)

    x = expand_conv(x, 64, k, strides=(2, 2))
    nb_conv += 2

    for i in range(N - 1):
        x = conv3_block(x, k, dropout)
        nb_conv += 2

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)

    x = keras.layers.AveragePooling2D((8, 8))(x)
    x = keras.layers.Flatten()(x)

    x = keras.layers.Dense(
        nb_classes,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        activation=activation,
    )(x)

    model = keras.Model(ip, x)

    if verbose:
        print(&#34;Wide Residual Network-%d-%d created.&#34; % (nb_conv, k))
    return model


if __name__ == &#34;__main__&#34;:

    init = (32, 32, 3)

    wrn_28_10 = create_wide_residual_network(init, nb_classes=10, N=2, k=2, dropout=0.0)

    wrn_28_10.summary()

    keras.utils.plot_model(
        wrn_28_10, &#34;WRN-16-2.png&#34;, show_shapes=True, show_layer_names=True
    )</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.vision.wrn.conv1_block"><code class="name flex">
<span>def <span class="ident">conv1_block</span></span>(<span>input, k=1, dropout=0.0)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def conv1_block(input, k=1, dropout=0.0):
    init = input

    channel_axis = 1 if K.image_data_format() == &#34;channels_first&#34; else -1

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(input)
    x = keras.layers.Activation(&#34;relu&#34;)(x)
    x = keras.layers.Convolution2D(
        16 * k,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(x)

    if dropout &gt; 0.0:
        x = keras.layers.Dropout(dropout)(x)

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)
    x = keras.layers.Convolution2D(
        16 * k,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(x)

    m = keras.layers.Add()([init, x])
    return m</code></pre>
</details>
</dd>
<dt id="ktrain.vision.wrn.conv2_block"><code class="name flex">
<span>def <span class="ident">conv2_block</span></span>(<span>input, k=1, dropout=0.0)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def conv2_block(input, k=1, dropout=0.0):
    init = input

    # channel_axis = 1 if K.image_dim_ordering() == &#34;th&#34; else -1
    channel_axis = -1

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(input)
    x = keras.layers.Activation(&#34;relu&#34;)(x)
    x = keras.layers.Convolution2D(
        32 * k,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(x)

    if dropout &gt; 0.0:
        x = keras.layers.Dropout(dropout)(x)

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)
    x = keras.layers.Convolution2D(
        32 * k,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(x)

    m = keras.layers.Add()([init, x])
    return m</code></pre>
</details>
</dd>
<dt id="ktrain.vision.wrn.conv3_block"><code class="name flex">
<span>def <span class="ident">conv3_block</span></span>(<span>input, k=1, dropout=0.0)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def conv3_block(input, k=1, dropout=0.0):
    init = input

    # channel_axis = 1 if K.image_dim_ordering() == &#34;th&#34; else -1
    channel_axis = -1

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(input)
    x = keras.layers.Activation(&#34;relu&#34;)(x)
    x = keras.layers.Convolution2D(
        64 * k,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(x)

    if dropout &gt; 0.0:
        x = keras.layers.Dropout(dropout)(x)

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)
    x = keras.layers.Convolution2D(
        64 * k,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(x)

    m = keras.layers.Add()([init, x])
    return m</code></pre>
</details>
</dd>
<dt id="ktrain.vision.wrn.create_wide_residual_network"><code class="name flex">
<span>def <span class="ident">create_wide_residual_network</span></span>(<span>input_dim, nb_classes=100, N=2, k=1, activation='softmax', dropout=0.0, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><p>Creates a Wide Residual Network with specified parameters</p>
<p>:param input: Input Keras object
:param nb_classes: Number of output classes
:param N: Depth of the network. Compute N = (n - 4) / 6.
Example : For a depth of 16, n = 16, N = (16 - 4) / 6 = 2
Example2: For a depth of 28, n = 28, N = (28 - 4) / 6 = 4
Example3: For a depth of 40, n = 40, N = (40 - 4) / 6 = 6
:param k: Width of the network.
:param dropout: Adds dropout if value is greater than 0.0
:param verbose: Debug info to describe created WRN
:return:</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def create_wide_residual_network(
    input_dim, nb_classes=100, N=2, k=1, activation=&#34;softmax&#34;, dropout=0.0, verbose=1
):
    &#34;&#34;&#34;
    Creates a Wide Residual Network with specified parameters

    :param input: Input Keras object
    :param nb_classes: Number of output classes
    :param N: Depth of the network. Compute N = (n - 4) / 6.
              Example : For a depth of 16, n = 16, N = (16 - 4) / 6 = 2
              Example2: For a depth of 28, n = 28, N = (28 - 4) / 6 = 4
              Example3: For a depth of 40, n = 40, N = (40 - 4) / 6 = 6
    :param k: Width of the network.
    :param dropout: Adds dropout if value is greater than 0.0
    :param verbose: Debug info to describe created WRN
    :return:
    &#34;&#34;&#34;
    channel_axis = 1 if K.image_data_format() == &#34;channels_first&#34; else -1

    ip = keras.layers.Input(shape=input_dim)

    x = initial_conv(ip)
    nb_conv = 4

    x = expand_conv(x, 16, k)
    nb_conv += 2

    for i in range(N - 1):
        x = conv1_block(x, k, dropout)
        nb_conv += 2

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)

    x = expand_conv(x, 32, k, strides=(2, 2))
    nb_conv += 2

    for i in range(N - 1):
        x = conv2_block(x, k, dropout)
        nb_conv += 2

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)

    x = expand_conv(x, 64, k, strides=(2, 2))
    nb_conv += 2

    for i in range(N - 1):
        x = conv3_block(x, k, dropout)
        nb_conv += 2

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)

    x = keras.layers.AveragePooling2D((8, 8))(x)
    x = keras.layers.Flatten()(x)

    x = keras.layers.Dense(
        nb_classes,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        activation=activation,
    )(x)

    model = keras.Model(ip, x)

    if verbose:
        print(&#34;Wide Residual Network-%d-%d created.&#34; % (nb_conv, k))
    return model</code></pre>
</details>
</dd>
<dt id="ktrain.vision.wrn.expand_conv"><code class="name flex">
<span>def <span class="ident">expand_conv</span></span>(<span>init, base, k, strides=(1, 1))</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def expand_conv(init, base, k, strides=(1, 1)):
    x = keras.layers.Convolution2D(
        base * k,
        (3, 3),
        padding=&#34;same&#34;,
        strides=strides,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(init)

    channel_axis = 1 if K.image_data_format() == &#34;channels_first&#34; else -1

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)

    x = keras.layers.Convolution2D(
        base * k,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(x)

    skip = keras.layers.Convolution2D(
        base * k,
        (1, 1),
        padding=&#34;same&#34;,
        strides=strides,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(init)

    m = keras.layers.Add()([x, skip])

    return m</code></pre>
</details>
</dd>
<dt id="ktrain.vision.wrn.initial_conv"><code class="name flex">
<span>def <span class="ident">initial_conv</span></span>(<span>input)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def initial_conv(input):
    x = keras.layers.Convolution2D(
        16,
        (3, 3),
        padding=&#34;same&#34;,
        kernel_initializer=&#34;he_normal&#34;,
        kernel_regularizer=keras.regularizers.l2(weight_decay),
        use_bias=False,
    )(input)

    channel_axis = 1 if K.image_data_format() == &#34;channels_first&#34; else -1

    x = keras.layers.BatchNormalization(
        axis=channel_axis, momentum=0.1, epsilon=1e-5, gamma_initializer=&#34;uniform&#34;
    )(x)
    x = keras.layers.Activation(&#34;relu&#34;)(x)
    return x</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain.vision" href="index.html">ktrain.vision</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="ktrain.vision.wrn.conv1_block" href="#ktrain.vision.wrn.conv1_block">conv1_block</a></code></li>
<li><code><a title="ktrain.vision.wrn.conv2_block" href="#ktrain.vision.wrn.conv2_block">conv2_block</a></code></li>
<li><code><a title="ktrain.vision.wrn.conv3_block" href="#ktrain.vision.wrn.conv3_block">conv3_block</a></code></li>
<li><code><a title="ktrain.vision.wrn.create_wide_residual_network" href="#ktrain.vision.wrn.create_wide_residual_network">create_wide_residual_network</a></code></li>
<li><code><a title="ktrain.vision.wrn.expand_conv" href="#ktrain.vision.wrn.expand_conv">expand_conv</a></code></li>
<li><code><a title="ktrain.vision.wrn.initial_conv" href="#ktrain.vision.wrn.initial_conv">initial_conv</a></code></li>
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