deeplabv3_plus.md

Multiclass semantic segmentation using DeepLabV3+

Author: Soumik Rakshit
Date created: 2021/08/31
Last modified: 2024/01/05
Description: Implement DeepLabV3+ architecture for Multi-class Semantic Segmentation.

View in Colab • GitHub source

---

Introduction

Semantic segmentation, with the goal to assign semantic labels to every pixel in an image, is an essential computer vision task. In this example, we implement the DeepLabV3+ model for multi-class semantic segmentation, a fully-convolutional architecture that performs well on semantic segmentation benchmarks.

References:

• Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

• Rethinking Atrous Convolution for Semantic Image Segmentation

• DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

---

Downloading the data

We will use the Crowd Instance-level Human Parsing Dataset for training our model. The Crowd Instance-level Human Parsing (CIHP) dataset has 38,280 diverse human images. Each image in CIHP is labeled with pixel-wise annotations for 20 categories, as well as instance-level identification. This dataset can be used for the "human part segmentation" task.

import keras
from keras import layers
from keras import ops

import os
import numpy as np
from glob import glob
import cv2
from scipy.io import loadmat
import matplotlib.pyplot as plt

# For data preprocessing
from tensorflow import image as tf_image
from tensorflow import data as tf_data
from tensorflow import io as tf_io

!gdown "1B9A9UCJYMwTL4oBEo4RZfbMZMaZhKJaz&confirm=t"
!unzip -q instance-level-human-parsing.zip

``` Downloading... From: https://drive.google.com/uc?id=1B9A9UCJYMwTL4oBEo4RZfbMZMaZhKJaz&confirm=t To: /content/keras-io/scripts/tmp_7009966/instance-level-human-parsing.zip 100% 2.91G/2.91G [00:22<00:00, 129MB/s]

</div>
---
## Creating a TensorFlow Dataset

Training on the entire CIHP dataset with 38,280 images takes a lot of time, hence we will be using
a smaller subset of 200 images for training our model in this example.


```python
IMAGE_SIZE = 512
BATCH_SIZE = 4
NUM_CLASSES = 20
DATA_DIR = "./instance-level_human_parsing/instance-level_human_parsing/Training"
NUM_TRAIN_IMAGES = 1000
NUM_VAL_IMAGES = 50

train_images = sorted(glob(os.path.join(DATA_DIR, "Images/*")))[:NUM_TRAIN_IMAGES]
train_masks = sorted(glob(os.path.join(DATA_DIR, "Category_ids/*")))[:NUM_TRAIN_IMAGES]
val_images = sorted(glob(os.path.join(DATA_DIR, "Images/*")))[
    NUM_TRAIN_IMAGES : NUM_VAL_IMAGES + NUM_TRAIN_IMAGES
]
val_masks = sorted(glob(os.path.join(DATA_DIR, "Category_ids/*")))[
    NUM_TRAIN_IMAGES : NUM_VAL_IMAGES + NUM_TRAIN_IMAGES
]


def read_image(image_path, mask=False):
    image = tf_io.read_file(image_path)
    if mask:
        image = tf_image.decode_png(image, channels=1)
        image.set_shape([None, None, 1])
        image = tf_image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])
    else:
        image = tf_image.decode_png(image, channels=3)
        image.set_shape([None, None, 3])
        image = tf_image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])
    return image


def load_data(image_list, mask_list):
    image = read_image(image_list)
    mask = read_image(mask_list, mask=True)
    return image, mask


def data_generator(image_list, mask_list):
    dataset = tf_data.Dataset.from_tensor_slices((image_list, mask_list))
    dataset = dataset.map(load_data, num_parallel_calls=tf_data.AUTOTUNE)
    dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
    return dataset


train_dataset = data_generator(train_images, train_masks)
val_dataset = data_generator(val_images, val_masks)

print("Train Dataset:", train_dataset)
print("Val Dataset:", val_dataset)

``` Train Dataset: <_BatchDataset element_spec=(TensorSpec(shape=(4, 512, 512, 3), dtype=tf.float32, name=None), TensorSpec(shape=(4, 512, 512, 1), dtype=tf.float32, name=None))> Val Dataset: <_BatchDataset element_spec=(TensorSpec(shape=(4, 512, 512, 3), dtype=tf.float32, name=None), TensorSpec(shape=(4, 512, 512, 1), dtype=tf.float32, name=None))>

</div>
---
## Building the DeepLabV3+ model

DeepLabv3+ extends DeepLabv3 by adding an encoder-decoder structure. The encoder module
processes multiscale contextual information by applying dilated convolution at multiple
scales, while the decoder module refines the segmentation results along object boundaries.

![](https://github.com/lattice-ai/DeepLabV3-Plus/raw/master/assets/deeplabv3_plus_diagram.png)

**Dilated convolution:** With dilated convolution, as we go deeper in the network, we can keep the
stride constant but with larger field-of-view without increasing the number of parameters
or the amount of computation. Besides, it enables larger output feature maps, which is
useful for semantic segmentation.

The reason for using **Dilated Spatial Pyramid Pooling** is that it was shown that as the
sampling rate becomes larger, the number of valid filter weights (i.e., weights that
are applied to the valid feature region, instead of padded zeros) becomes smaller.


```python

def convolution_block(
    block_input,
    num_filters=256,
    kernel_size=3,
    dilation_rate=1,
    use_bias=False,
):
    x = layers.Conv2D(
        num_filters,
        kernel_size=kernel_size,
        dilation_rate=dilation_rate,
        padding="same",
        use_bias=use_bias,
        kernel_initializer=keras.initializers.HeNormal(),
    )(block_input)
    x = layers.BatchNormalization()(x)
    return ops.nn.relu(x)


def DilatedSpatialPyramidPooling(dspp_input):
    dims = dspp_input.shape
    x = layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)
    x = convolution_block(x, kernel_size=1, use_bias=True)
    out_pool = layers.UpSampling2D(
        size=(dims[-3] // x.shape[1], dims[-2] // x.shape[2]),
        interpolation="bilinear",
    )(x)

    out_1 = convolution_block(dspp_input, kernel_size=1, dilation_rate=1)
    out_6 = convolution_block(dspp_input, kernel_size=3, dilation_rate=6)
    out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12)
    out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18)

    x = layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])
    output = convolution_block(x, kernel_size=1)
    return output

The encoder features are first bilinearly upsampled by a factor 4, and then concatenated with the corresponding low-level features from the network backbone that have the same spatial resolution. For this example, we use a ResNet50 pretrained on ImageNet as the backbone model, and we use the low-level features from the conv4_block6_2_relu block of the backbone.

def DeeplabV3Plus(image_size, num_classes):
    model_input = keras.Input(shape=(image_size, image_size, 3))
    preprocessed = keras.applications.resnet50.preprocess_input(model_input)
    resnet50 = keras.applications.ResNet50(
        weights="imagenet", include_top=False, input_tensor=preprocessed
    )
    x = resnet50.get_layer("conv4_block6_2_relu").output
    x = DilatedSpatialPyramidPooling(x)

    input_a = layers.UpSampling2D(
        size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),
        interpolation="bilinear",
    )(x)
    input_b = resnet50.get_layer("conv2_block3_2_relu").output
    input_b = convolution_block(input_b, num_filters=48, kernel_size=1)

    x = layers.Concatenate(axis=-1)([input_a, input_b])
    x = convolution_block(x)
    x = convolution_block(x)
    x = layers.UpSampling2D(
        size=(image_size // x.shape[1], image_size // x.shape[2]),
        interpolation="bilinear",
    )(x)
    model_output = layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x)
    return keras.Model(inputs=model_input, outputs=model_output)


model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES)
model.summary()

``` Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5 94765736/94765736 ━━━━━━━━━━━━━━━━━━━━ 1s 0us/step

</div>
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold">Model: "functional_1"</span>
</pre>




<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃<span style="font-weight: bold"> Layer (type)               </span>┃<span style="font-weight: bold"> Output Shape           </span>┃<span style="font-weight: bold">   Param # </span>┃<span style="font-weight: bold"> Connected to                </span>┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ input_layer (<span style="color: #0087ff; text-decoration-color: #0087ff">InputLayer</span>)   │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>, <span style="color: #00af00; text-decoration-color: #00af00">3</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ -                           │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ get_item (<span style="color: #0087ff; text-decoration-color: #0087ff">GetItem</span>)         │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)       │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ input_layer[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]           │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ get_item_1 (<span style="color: #0087ff; text-decoration-color: #0087ff">GetItem</span>)       │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)       │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ input_layer[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]           │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ get_item_2 (<span style="color: #0087ff; text-decoration-color: #0087ff">GetItem</span>)       │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)       │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ input_layer[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]           │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ stack (<span style="color: #0087ff; text-decoration-color: #0087ff">Stack</span>)              │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>, <span style="color: #00af00; text-decoration-color: #00af00">3</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ get_item[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],             │
│                            │                        │           │ get_item_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],           │
│                            │                        │           │ get_item_2[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]            │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ add (<span style="color: #0087ff; text-decoration-color: #0087ff">Add</span>)                  │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>, <span style="color: #00af00; text-decoration-color: #00af00">3</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ stack[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]                 │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv1_pad (<span style="color: #0087ff; text-decoration-color: #0087ff">ZeroPadding2D</span>)  │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">518</span>, <span style="color: #00af00; text-decoration-color: #00af00">518</span>, <span style="color: #00af00; text-decoration-color: #00af00">3</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ add[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]                   │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv1_conv (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │     <span style="color: #00af00; text-decoration-color: #00af00">9,472</span> │ conv1_pad[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv1_bn                   │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │       <span style="color: #00af00; text-decoration-color: #00af00">256</span> │ conv1_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]            │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv1_relu (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)    │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv1_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]              │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ pool1_pad (<span style="color: #0087ff; text-decoration-color: #0087ff">ZeroPadding2D</span>)  │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">258</span>, <span style="color: #00af00; text-decoration-color: #00af00">258</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv1_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]            │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ pool1_pool (<span style="color: #0087ff; text-decoration-color: #0087ff">MaxPooling2D</span>)  │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ pool1_pad[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block1_1_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │     <span style="color: #00af00; text-decoration-color: #00af00">4,160</span> │ pool1_pool[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]            │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block1_1_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │       <span style="color: #00af00; text-decoration-color: #00af00">256</span> │ conv2_block1_1_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block1_1_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv2_block1_1_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block1_2_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │    <span style="color: #00af00; text-decoration-color: #00af00">36,928</span> │ conv2_block1_1_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block1_2_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │       <span style="color: #00af00; text-decoration-color: #00af00">256</span> │ conv2_block1_2_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block1_2_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv2_block1_2_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block1_0_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │    <span style="color: #00af00; text-decoration-color: #00af00">16,640</span> │ pool1_pool[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]            │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block1_3_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │    <span style="color: #00af00; text-decoration-color: #00af00">16,640</span> │ conv2_block1_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block1_0_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv2_block1_0_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block1_3_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv2_block1_3_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block1_add (<span style="color: #0087ff; text-decoration-color: #0087ff">Add</span>)     │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv2_block1_0_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],    │
│                            │                        │           │ conv2_block1_3_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block1_out           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv2_block1_add[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block2_1_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │    <span style="color: #00af00; text-decoration-color: #00af00">16,448</span> │ conv2_block1_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block2_1_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │       <span style="color: #00af00; text-decoration-color: #00af00">256</span> │ conv2_block2_1_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block2_1_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv2_block2_1_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block2_2_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │    <span style="color: #00af00; text-decoration-color: #00af00">36,928</span> │ conv2_block2_1_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block2_2_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │       <span style="color: #00af00; text-decoration-color: #00af00">256</span> │ conv2_block2_2_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block2_2_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv2_block2_2_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block2_3_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │    <span style="color: #00af00; text-decoration-color: #00af00">16,640</span> │ conv2_block2_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block2_3_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv2_block2_3_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block2_add (<span style="color: #0087ff; text-decoration-color: #0087ff">Add</span>)     │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv2_block1_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],     │
│                            │                        │           │ conv2_block2_3_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block2_out           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv2_block2_add[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block3_1_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │    <span style="color: #00af00; text-decoration-color: #00af00">16,448</span> │ conv2_block2_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block3_1_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │       <span style="color: #00af00; text-decoration-color: #00af00">256</span> │ conv2_block3_1_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block3_1_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv2_block3_1_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block3_2_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │    <span style="color: #00af00; text-decoration-color: #00af00">36,928</span> │ conv2_block3_1_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block3_2_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │       <span style="color: #00af00; text-decoration-color: #00af00">256</span> │ conv2_block3_2_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block3_2_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv2_block3_2_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block3_3_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │    <span style="color: #00af00; text-decoration-color: #00af00">16,640</span> │ conv2_block3_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block3_3_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv2_block3_3_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block3_add (<span style="color: #0087ff; text-decoration-color: #0087ff">Add</span>)     │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv2_block2_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],     │
│                            │                        │           │ conv2_block3_3_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2_block3_out           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv2_block3_add[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block1_1_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │    <span style="color: #00af00; text-decoration-color: #00af00">32,896</span> │ conv2_block3_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block1_1_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │       <span style="color: #00af00; text-decoration-color: #00af00">512</span> │ conv3_block1_1_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block1_1_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block1_1_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block1_2_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">147,584</span> │ conv3_block1_1_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block1_2_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │       <span style="color: #00af00; text-decoration-color: #00af00">512</span> │ conv3_block1_2_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block1_2_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block1_2_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block1_0_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">131,584</span> │ conv2_block3_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block1_3_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │    <span style="color: #00af00; text-decoration-color: #00af00">66,048</span> │ conv3_block1_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block1_0_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">2,048</span> │ conv3_block1_0_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block1_3_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">2,048</span> │ conv3_block1_3_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block1_add (<span style="color: #0087ff; text-decoration-color: #0087ff">Add</span>)     │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block1_0_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],    │
│                            │                        │           │ conv3_block1_3_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block1_out           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block1_add[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block2_1_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │    <span style="color: #00af00; text-decoration-color: #00af00">65,664</span> │ conv3_block1_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block2_1_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │       <span style="color: #00af00; text-decoration-color: #00af00">512</span> │ conv3_block2_1_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block2_1_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block2_1_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block2_2_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">147,584</span> │ conv3_block2_1_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block2_2_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │       <span style="color: #00af00; text-decoration-color: #00af00">512</span> │ conv3_block2_2_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block2_2_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block2_2_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block2_3_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │    <span style="color: #00af00; text-decoration-color: #00af00">66,048</span> │ conv3_block2_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block2_3_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">2,048</span> │ conv3_block2_3_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block2_add (<span style="color: #0087ff; text-decoration-color: #0087ff">Add</span>)     │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block1_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],     │
│                            │                        │           │ conv3_block2_3_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block2_out           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block2_add[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block3_1_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │    <span style="color: #00af00; text-decoration-color: #00af00">65,664</span> │ conv3_block2_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block3_1_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │       <span style="color: #00af00; text-decoration-color: #00af00">512</span> │ conv3_block3_1_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block3_1_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block3_1_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block3_2_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">147,584</span> │ conv3_block3_1_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block3_2_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │       <span style="color: #00af00; text-decoration-color: #00af00">512</span> │ conv3_block3_2_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block3_2_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block3_2_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block3_3_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │    <span style="color: #00af00; text-decoration-color: #00af00">66,048</span> │ conv3_block3_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block3_3_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">2,048</span> │ conv3_block3_3_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block3_add (<span style="color: #0087ff; text-decoration-color: #0087ff">Add</span>)     │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block2_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],     │
│                            │                        │           │ conv3_block3_3_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block3_out           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block3_add[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block4_1_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │    <span style="color: #00af00; text-decoration-color: #00af00">65,664</span> │ conv3_block3_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block4_1_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │       <span style="color: #00af00; text-decoration-color: #00af00">512</span> │ conv3_block4_1_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block4_1_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block4_1_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block4_2_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">147,584</span> │ conv3_block4_1_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block4_2_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │       <span style="color: #00af00; text-decoration-color: #00af00">512</span> │ conv3_block4_2_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block4_2_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block4_2_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block4_3_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │    <span style="color: #00af00; text-decoration-color: #00af00">66,048</span> │ conv3_block4_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block4_3_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">2,048</span> │ conv3_block4_3_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block4_add (<span style="color: #0087ff; text-decoration-color: #0087ff">Add</span>)     │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block3_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],     │
│                            │                        │           │ conv3_block4_3_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv3_block4_out           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv3_block4_add[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block1_1_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">131,328</span> │ conv3_block4_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block1_1_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv4_block1_1_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block1_1_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block1_1_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block1_2_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">590,080</span> │ conv4_block1_1_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block1_2_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv4_block1_2_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block1_2_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block1_2_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block1_0_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │   <span style="color: #00af00; text-decoration-color: #00af00">525,312</span> │ conv3_block4_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block1_3_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │   <span style="color: #00af00; text-decoration-color: #00af00">263,168</span> │ conv4_block1_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block1_0_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │     <span style="color: #00af00; text-decoration-color: #00af00">4,096</span> │ conv4_block1_0_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block1_3_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │     <span style="color: #00af00; text-decoration-color: #00af00">4,096</span> │ conv4_block1_3_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block1_add (<span style="color: #0087ff; text-decoration-color: #0087ff">Add</span>)     │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block1_0_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],    │
│                            │                        │           │ conv4_block1_3_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block1_out           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block1_add[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block2_1_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">262,400</span> │ conv4_block1_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block2_1_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv4_block2_1_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block2_1_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block2_1_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block2_2_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">590,080</span> │ conv4_block2_1_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block2_2_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv4_block2_2_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block2_2_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block2_2_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block2_3_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │   <span style="color: #00af00; text-decoration-color: #00af00">263,168</span> │ conv4_block2_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block2_3_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │     <span style="color: #00af00; text-decoration-color: #00af00">4,096</span> │ conv4_block2_3_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block2_add (<span style="color: #0087ff; text-decoration-color: #0087ff">Add</span>)     │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block1_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],     │
│                            │                        │           │ conv4_block2_3_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block2_out           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block2_add[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block3_1_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">262,400</span> │ conv4_block2_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block3_1_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv4_block3_1_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block3_1_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block3_1_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block3_2_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">590,080</span> │ conv4_block3_1_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block3_2_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv4_block3_2_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block3_2_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block3_2_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block3_3_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │   <span style="color: #00af00; text-decoration-color: #00af00">263,168</span> │ conv4_block3_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block3_3_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │     <span style="color: #00af00; text-decoration-color: #00af00">4,096</span> │ conv4_block3_3_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block3_add (<span style="color: #0087ff; text-decoration-color: #0087ff">Add</span>)     │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block2_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],     │
│                            │                        │           │ conv4_block3_3_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block3_out           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block3_add[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block4_1_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">262,400</span> │ conv4_block3_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block4_1_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv4_block4_1_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block4_1_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block4_1_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block4_2_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">590,080</span> │ conv4_block4_1_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block4_2_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv4_block4_2_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block4_2_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block4_2_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block4_3_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │   <span style="color: #00af00; text-decoration-color: #00af00">263,168</span> │ conv4_block4_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block4_3_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │     <span style="color: #00af00; text-decoration-color: #00af00">4,096</span> │ conv4_block4_3_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block4_add (<span style="color: #0087ff; text-decoration-color: #0087ff">Add</span>)     │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block3_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],     │
│                            │                        │           │ conv4_block4_3_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block4_out           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block4_add[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block5_1_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">262,400</span> │ conv4_block4_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block5_1_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv4_block5_1_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block5_1_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block5_1_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block5_2_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">590,080</span> │ conv4_block5_1_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block5_2_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv4_block5_2_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block5_2_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block5_2_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block5_3_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │   <span style="color: #00af00; text-decoration-color: #00af00">263,168</span> │ conv4_block5_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block5_3_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │     <span style="color: #00af00; text-decoration-color: #00af00">4,096</span> │ conv4_block5_3_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block5_add (<span style="color: #0087ff; text-decoration-color: #0087ff">Add</span>)     │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block4_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],     │
│                            │                        │           │ conv4_block5_3_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block5_out           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block5_add[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block6_1_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">262,400</span> │ conv4_block5_out[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block6_1_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv4_block6_1_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block6_1_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block6_1_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block6_2_conv        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">590,080</span> │ conv4_block6_1_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                   │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block6_2_bn          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv4_block6_2_conv[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv4_block6_2_relu        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block6_2_bn[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Activation</span>)               │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ average_pooling2d          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)      │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv4_block6_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">AveragePooling2D</span>)         │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2d (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)            │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)      │    <span style="color: #00af00; text-decoration-color: #00af00">65,792</span> │ average_pooling2d[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ batch_normalization        │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)      │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv2d[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]                │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2d_1 (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │    <span style="color: #00af00; text-decoration-color: #00af00">65,536</span> │ conv4_block6_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2d_2 (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">589,824</span> │ conv4_block6_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2d_3 (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">589,824</span> │ conv4_block6_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2d_4 (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">589,824</span> │ conv4_block6_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ relu (<span style="color: #0087ff; text-decoration-color: #0087ff">Relu</span>)                │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)      │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ batch_normalization[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ batch_normalization_1      │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv2d_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]              │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ batch_normalization_2      │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv2d_2[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]              │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ batch_normalization_3      │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv2d_3[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]              │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ batch_normalization_4      │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv2d_4[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]              │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ up_sampling2d              │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]                  │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">UpSampling2D</span>)             │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ relu_1 (<span style="color: #0087ff; text-decoration-color: #0087ff">Relu</span>)              │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ batch_normalization_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ relu_2 (<span style="color: #0087ff; text-decoration-color: #0087ff">Relu</span>)              │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ batch_normalization_2[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ relu_3 (<span style="color: #0087ff; text-decoration-color: #0087ff">Relu</span>)              │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ batch_normalization_3[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ relu_4 (<span style="color: #0087ff; text-decoration-color: #0087ff">Relu</span>)              │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ batch_normalization_4[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ concatenate (<span style="color: #0087ff; text-decoration-color: #0087ff">Concatenate</span>)  │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">1280</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ up_sampling2d[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],        │
│                            │                        │           │ relu_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>], relu_2[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>], │
│                            │                        │           │ relu_3[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>], relu_4[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]  │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2d_5 (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │   <span style="color: #00af00; text-decoration-color: #00af00">327,680</span> │ concatenate[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]           │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ batch_normalization_5      │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv2d_5[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]              │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2d_6 (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">48</span>)   │     <span style="color: #00af00; text-decoration-color: #00af00">3,072</span> │ conv2_block3_2_relu[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ relu_5 (<span style="color: #0087ff; text-decoration-color: #0087ff">Relu</span>)              │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)    │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ batch_normalization_5[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ batch_normalization_6      │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">48</span>)   │       <span style="color: #00af00; text-decoration-color: #00af00">192</span> │ conv2d_6[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]              │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ up_sampling2d_1            │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ relu_5[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]                │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">UpSampling2D</span>)             │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ relu_6 (<span style="color: #0087ff; text-decoration-color: #0087ff">Relu</span>)              │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">48</span>)   │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ batch_normalization_6[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ concatenate_1              │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">304</span>)  │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ up_sampling2d_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],      │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Concatenate</span>)              │                        │           │ relu_6[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]                │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2d_7 (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │   <span style="color: #00af00; text-decoration-color: #00af00">700,416</span> │ concatenate_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]         │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ batch_normalization_7      │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv2d_7[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]              │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ relu_7 (<span style="color: #0087ff; text-decoration-color: #0087ff">Relu</span>)              │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ batch_normalization_7[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2d_8 (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │   <span style="color: #00af00; text-decoration-color: #00af00">589,824</span> │ relu_7[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]                │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ batch_normalization_8      │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │     <span style="color: #00af00; text-decoration-color: #00af00">1,024</span> │ conv2d_8[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]              │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">BatchNormalization</span>)       │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ relu_8 (<span style="color: #0087ff; text-decoration-color: #0087ff">Relu</span>)              │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ batch_normalization_8[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ up_sampling2d_2            │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>)  │         <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ relu_8[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]                │
│ (<span style="color: #0087ff; text-decoration-color: #0087ff">UpSampling2D</span>)             │                        │           │                             │
├────────────────────────────┼────────────────────────┼───────────┼─────────────────────────────┤
│ conv2d_9 (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)          │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>, <span style="color: #00af00; text-decoration-color: #00af00">512</span>, <span style="color: #00af00; text-decoration-color: #00af00">20</span>)   │     <span style="color: #00af00; text-decoration-color: #00af00">5,140</span> │ up_sampling2d_2[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]       │
└────────────────────────────┴────────────────────────┴───────────┴─────────────────────────────┘
</pre>




<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Total params: </span><span style="color: #00af00; text-decoration-color: #00af00">11,857,236</span> (45.23 MB)
</pre>




<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Trainable params: </span><span style="color: #00af00; text-decoration-color: #00af00">11,824,500</span> (45.11 MB)
</pre>




<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Non-trainable params: </span><span style="color: #00af00; text-decoration-color: #00af00">32,736</span> (127.88 KB)
</pre>



---
## Training

We train the model using sparse categorical crossentropy as the loss function, and
Adam as the optimizer.


```python
loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=0.001),
    loss=loss,
    metrics=["accuracy"],
)

history = model.fit(train_dataset, validation_data=val_dataset, epochs=25)

plt.plot(history.history["loss"])
plt.title("Training Loss")
plt.ylabel("loss")
plt.xlabel("epoch")
plt.show()

plt.plot(history.history["accuracy"])
plt.title("Training Accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.show()

plt.plot(history.history["val_loss"])
plt.title("Validation Loss")
plt.ylabel("val_loss")
plt.xlabel("epoch")
plt.show()

plt.plot(history.history["val_accuracy"])
plt.title("Validation Accuracy")
plt.ylabel("val_accuracy")
plt.xlabel("epoch")
plt.show()

``` Epoch 1/25 250/250 [==============================] - 115s 359ms/step - loss: 1.1765 - accuracy: 0.6424 - val_loss: 2.3559 - val_accuracy: 0.5960 Epoch 2/25 250/250 [==============================] - 92s 366ms/step - loss: 0.9413 - accuracy: 0.6998 - val_loss: 1.7349 - val_accuracy: 0.5593 Epoch 3/25 250/250 [==============================] - 93s 371ms/step - loss: 0.8415 - accuracy: 0.7310 - val_loss: 1.3097 - val_accuracy: 0.6281 Epoch 4/25 250/250 [==============================] - 93s 372ms/step - loss: 0.7640 - accuracy: 0.7552 - val_loss: 1.0175 - val_accuracy: 0.6885 Epoch 5/25 250/250 [==============================] - 93s 372ms/step - loss: 0.7139 - accuracy: 0.7706 - val_loss: 1.2226 - val_accuracy: 0.6107 Epoch 6/25 250/250 [==============================] - 93s 373ms/step - loss: 0.6647 - accuracy: 0.7867 - val_loss: 0.8583 - val_accuracy: 0.7178 Epoch 7/25 250/250 [==============================] - 94s 375ms/step - loss: 0.5986 - accuracy: 0.8080 - val_loss: 0.9724 - val_accuracy: 0.7135 Epoch 8/25 250/250 [==============================] - 93s 372ms/step - loss: 0.5599 - accuracy: 0.8212 - val_loss: 0.9722 - val_accuracy: 0.7064 Epoch 9/25 250/250 [==============================] - 93s 372ms/step - loss: 0.5161 - accuracy: 0.8364 - val_loss: 0.9023 - val_accuracy: 0.7471 Epoch 10/25 250/250 [==============================] - 93s 373ms/step - loss: 0.4719 - accuracy: 0.8515 - val_loss: 0.8803 - val_accuracy: 0.7540 Epoch 11/25 250/250 [==============================] - 93s 372ms/step - loss: 0.4337 - accuracy: 0.8636 - val_loss: 0.9682 - val_accuracy: 0.7377 Epoch 12/25 250/250 [==============================] - 93s 373ms/step - loss: 0.4079 - accuracy: 0.8718 - val_loss: 0.9586 - val_accuracy: 0.7551 Epoch 13/25 250/250 [==============================] - 93s 373ms/step - loss: 0.3694 - accuracy: 0.8856 - val_loss: 0.9676 - val_accuracy: 0.7606 Epoch 14/25 250/250 [==============================] - 93s 373ms/step - loss: 0.3493 - accuracy: 0.8913 - val_loss: 0.8375 - val_accuracy: 0.7706 Epoch 15/25 250/250 [==============================] - 93s 373ms/step - loss: 0.3217 - accuracy: 0.9008 - val_loss: 0.9956 - val_accuracy: 0.7469 Epoch 16/25 250/250 [==============================] - 93s 372ms/step - loss: 0.3018 - accuracy: 0.9075 - val_loss: 0.9614 - val_accuracy: 0.7474 Epoch 17/25 250/250 [==============================] - 93s 372ms/step - loss: 0.2870 - accuracy: 0.9122 - val_loss: 0.9652 - val_accuracy: 0.7626 Epoch 18/25 250/250 [==============================] - 93s 373ms/step - loss: 0.2685 - accuracy: 0.9182 - val_loss: 0.8913 - val_accuracy: 0.7824 Epoch 19/25 250/250 [==============================] - 93s 373ms/step - loss: 0.2574 - accuracy: 0.9216 - val_loss: 1.0205 - val_accuracy: 0.7417 Epoch 20/25 250/250 [==============================] - 93s 372ms/step - loss: 0.2619 - accuracy: 0.9199 - val_loss: 0.9237 - val_accuracy: 0.7788 Epoch 21/25 250/250 [==============================] - 93s 372ms/step - loss: 0.2372 - accuracy: 0.9280 - val_loss: 0.9076 - val_accuracy: 0.7796 Epoch 22/25 250/250 [==============================] - 93s 372ms/step - loss: 0.2175 - accuracy: 0.9344 - val_loss: 0.9797 - val_accuracy: 0.7742 Epoch 23/25 250/250 [==============================] - 93s 372ms/step - loss: 0.2084 - accuracy: 0.9370 - val_loss: 0.9981 - val_accuracy: 0.7870 Epoch 24/25 250/250 [==============================] - 93s 373ms/step - loss: 0.2077 - accuracy: 0.9370 - val_loss: 1.0494 - val_accuracy: 0.7767 Epoch 25/25 250/250 [==============================] - 93s 372ms/step - loss: 0.2059 - accuracy: 0.9377 - val_loss: 0.9640 - val_accuracy: 0.7651

</div>
    
![png](/img/examples/vision/deeplabv3_plus/deeplabv3_plus_12_1.png)
    



    
![png](/img/examples/vision/deeplabv3_plus/deeplabv3_plus_12_2.png)
    



    
![png](/img/examples/vision/deeplabv3_plus/deeplabv3_plus_12_3.png)
    



    
![png](/img/examples/vision/deeplabv3_plus/deeplabv3_plus_12_4.png)
    


---
## Inference using Colormap Overlay

The raw predictions from the model represent a one-hot encoded tensor of shape `(N, 512, 512, 20)`
where each one of the 20 channels is a binary mask corresponding to a predicted label.
In order to visualize the results, we plot them as RGB segmentation masks where each pixel
is represented by a unique color corresponding to the particular label predicted. We can easily
find the color corresponding to each label from the `human_colormap.mat` file provided as part
of the dataset. We would also plot an overlay of the RGB segmentation mask on the input image as
this further helps us to identify the different categories present in the image more intuitively.


```python
# Loading the Colormap
colormap = loadmat(
    "./instance-level_human_parsing/instance-level_human_parsing/human_colormap.mat"
)["colormap"]
colormap = colormap * 100
colormap = colormap.astype(np.uint8)


def infer(model, image_tensor):
    predictions = model.predict(np.expand_dims((image_tensor), axis=0))
    predictions = np.squeeze(predictions)
    predictions = np.argmax(predictions, axis=2)
    return predictions


def decode_segmentation_masks(mask, colormap, n_classes):
    r = np.zeros_like(mask).astype(np.uint8)
    g = np.zeros_like(mask).astype(np.uint8)
    b = np.zeros_like(mask).astype(np.uint8)
    for l in range(0, n_classes):
        idx = mask == l
        r[idx] = colormap[l, 0]
        g[idx] = colormap[l, 1]
        b[idx] = colormap[l, 2]
    rgb = np.stack([r, g, b], axis=2)
    return rgb


def get_overlay(image, colored_mask):
    image = keras.utils.array_to_img(image)
    image = np.array(image).astype(np.uint8)
    overlay = cv2.addWeighted(image, 0.35, colored_mask, 0.65, 0)
    return overlay


def plot_samples_matplotlib(display_list, figsize=(5, 3)):
    _, axes = plt.subplots(nrows=1, ncols=len(display_list), figsize=figsize)
    for i in range(len(display_list)):
        if display_list[i].shape[-1] == 3:
            axes[i].imshow(keras.utils.array_to_img(display_list[i]))
        else:
            axes[i].imshow(display_list[i])
    plt.show()


def plot_predictions(images_list, colormap, model):
    for image_file in images_list:
        image_tensor = read_image(image_file)
        prediction_mask = infer(image_tensor=image_tensor, model=model)
        prediction_colormap = decode_segmentation_masks(prediction_mask, colormap, 20)
        overlay = get_overlay(image_tensor, prediction_colormap)
        plot_samples_matplotlib(
            [image_tensor, overlay, prediction_colormap], figsize=(18, 14)
        )

Inference on Train Images

plot_predictions(train_images[:4], colormap, model=model)

``` 1/1 ━━━━━━━━━━━━━━━━━━━━ 7s 7s/step

</div>
    
![png](/img/examples/vision/deeplabv3_plus/deeplabv3_plus_16_1.png)
    


<div class="k-default-codeblock">

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step

</div>
    
![png](/img/examples/vision/deeplabv3_plus/deeplabv3_plus_16_3.png)
    


<div class="k-default-codeblock">

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step

</div>
    
![png](/img/examples/vision/deeplabv3_plus/deeplabv3_plus_16_5.png)
    


<div class="k-default-codeblock">

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step

</div>
    
![png](/img/examples/vision/deeplabv3_plus/deeplabv3_plus_16_7.png)
    


### Inference on Validation Images

You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/deeplabv3p-resnet50)
and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/Human-Part-Segmentation).


```python
plot_predictions(val_images[:4], colormap, model=model)

``` 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step

</div>
    
![png](/img/examples/vision/deeplabv3_plus/deeplabv3_plus_18_0.png)
    


<div class="k-default-codeblock">

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step

</div>
    
![png](/img/examples/vision/deeplabv3_plus/deeplabv3_plus_18_1.png)
    


<div class="k-default-codeblock">

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step

</div>
    
![png](/img/examples/vision/deeplabv3_plus/deeplabv3_plus_18_2.png)
    


<div class="k-default-codeblock">

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step

</div>
    
![png](/img/examples/vision/deeplabv3_plus/deeplabv3_plus_18_3.png)