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"<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\"</span>\n",
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"\u001b[1mModel: \"functional\"\u001b[0m\n"
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃<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>┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ 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> │ - │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ 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\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation (<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\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ 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\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │ activation[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_1 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv2d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_1 (<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\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_1… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d │ (<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> │ activation_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ 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\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │ max_pooling2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_2 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv2d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_2 (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ 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\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ activation_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_3 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv2d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_3 (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_3… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ 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\">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\">295,168</span> │ max_pooling2d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_4 │ (<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_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_4 (<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\">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_4… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ 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\">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\">590,080</span> │ activation_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_5 │ (<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_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_5 (<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\">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_5… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_2 │ (<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\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ 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\">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\">1,180,160</span> │ max_pooling2d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_6 │ (<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> │ conv2d_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_6 (<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\">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> │ batch_normalization_6… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ 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\">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\">2,359,808</span> │ activation_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_7 │ (<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> │ conv2d_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_7 (<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\">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> │ batch_normalization_7… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_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\">512</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ 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\">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\">4,719,616</span> │ max_pooling2d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_8 │ (<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> │ conv2d_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_8 (<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\">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> │ batch_normalization_8… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ 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\">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\">9,438,208</span> │ activation_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_9 │ (<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> │ conv2d_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_9 (<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\">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> │ batch_normalization_9… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose │ (<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\">2,097,664</span> │ activation_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ 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\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
"│ │ │ │ activation_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_10 (<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\">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\">4,719,104</span> │ concatenate[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_10 │ (<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> │ conv2d_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_10 │ (<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> │ batch_normalization_1… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_11 (<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\">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\">2,359,808</span> │ activation_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_11 │ (<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> │ conv2d_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_11 │ (<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> │ batch_normalization_1… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_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\">524,544</span> │ activation_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ 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\">512</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>) │ │ │ activation_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_12 (<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\">1,179,904</span> │ concatenate_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_12 │ (<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_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_12 │ (<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_1… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_13 (<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\">590,080</span> │ activation_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_13 │ (<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_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_13 │ (<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_1… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_2 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">131,200</span> │ activation_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ concatenate_2 │ (<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\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>) │ │ │ activation_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_14 (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">295,040</span> │ concatenate_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_14 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv2d_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_14 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_1… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_15 (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ activation_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_15 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv2d_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_15 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_1… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_3 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32,832</span> │ activation_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ concatenate_3 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>) │ │ │ activation_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_16 (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">73,792</span> │ concatenate_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_16 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv2d_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_16 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_1… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_17 (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │ activation_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_17 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv2d_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_17 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_1… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_18 (<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\">11</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">715</span> │ activation_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
"</pre>\n"
],
"text/plain": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m1,792\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv2d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization[\u001b[38;5;34m0\u001b[0m… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │ activation[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv2d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_1 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │ max_pooling2d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv2d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_2 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │ activation_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv2d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_3 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_3… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m295,168\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv2d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_4 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_4… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │ activation_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv2d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_5 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_5… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,180,160\u001b[0m │ max_pooling2d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv2d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_6 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_6… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ activation_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv2d_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_7 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_7… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,719,616\u001b[0m │ max_pooling2d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │ conv2d_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_8 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_8… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m9,438,208\u001b[0m │ activation_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_9 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │ conv2d_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_9 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_9… │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,097,664\u001b[0m │ activation_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mConv2DTranspose\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ concatenate (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2d_transpose[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
"│ │ │ │ activation_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m4,719,104\u001b[0m │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_10 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv2d_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_10 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ activation_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_11 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv2d_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_11 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m524,544\u001b[0m │ activation_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mConv2DTranspose\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ concatenate_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2d_transpose_1[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,179,904\u001b[0m │ concatenate_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_12 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv2d_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_12 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │ activation_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_13 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv2d_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_13 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m131,200\u001b[0m │ activation_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mConv2DTranspose\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ concatenate_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2d_transpose_2[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m295,040\u001b[0m │ concatenate_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_14 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv2d_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_14 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │ activation_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_15 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv2d_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_15 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m32,832\u001b[0m │ activation_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mConv2DTranspose\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ concatenate_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2d_transpose_3[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m73,792\u001b[0m │ concatenate_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_16 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv2d_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_16 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │ activation_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_17 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv2d_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_17 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_18 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m11\u001b[0m) │ \u001b[38;5;34m715\u001b[0m │ activation_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n"
]
},
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"<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\">31,055,947</span> (118.47 MB)\n",
"</pre>\n"
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"text/plain": [
"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m31,055,947\u001b[0m (118.47 MB)\n"
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"<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\">31,044,171</span> (118.42 MB)\n",
"</pre>\n"
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"text/plain": [
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m31,044,171\u001b[0m (118.42 MB)\n"
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"<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\">11,776</span> (46.00 KB)\n",
"</pre>\n"
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"text/plain": [
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m11,776\u001b[0m (46.00 KB)\n"
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"metadata": {},
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],
"source": [
"import os\n",
"\n",
"from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Conv2DTranspose, Concatenate, Input\n",
"from tensorflow.keras.models import Model\n",
"import numpy as np\n",
"import cv2\n",
"from glob import glob\n",
"import tensorflow as tf\n",
"from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping, CSVLogger\n",
"\n",
"def conv_block(inputs, num_filters):\n",
" x = Conv2D(num_filters, 3, padding=\"same\")(inputs)\n",
" x = BatchNormalization()(x)\n",
" x = Activation(\"relu\")(x)\n",
"\n",
" x = Conv2D(num_filters, 3, padding=\"same\")(x)\n",
" x = BatchNormalization()(x)\n",
" x = Activation(\"relu\")(x)\n",
"\n",
" return x\n",
"\n",
"def encoder_block(inputs, num_filters):\n",
" x = conv_block(inputs, num_filters)\n",
" p = MaxPool2D((2, 2))(x)\n",
" return x, p\n",
"\n",
"def decoder_block(inputs, skip, num_filters):\n",
" x = Conv2DTranspose(num_filters, (2, 2), strides=2, padding=\"same\")(inputs)\n",
" x = Concatenate()([x, skip])\n",
" x = conv_block(x, num_filters)\n",
" return x\n",
"\n",
"def build_unet(input_shape, num_classes):\n",
" inputs = Input(input_shape)\n",
"\n",
" s1, p1 = encoder_block(inputs, 64)\n",
" s2, p2 = encoder_block(p1, 128)\n",
" s3, p3 = encoder_block(p2, 256)\n",
" s4, p4 = encoder_block(p3, 512)\n",
"\n",
" b1 = conv_block(p4, 1024)\n",
"\n",
" d1 = decoder_block(b1, s4, 512)\n",
" d2 = decoder_block(d1, s3, 256)\n",
" d3 = decoder_block(d2, s2, 128)\n",
" d4 = decoder_block(d3, s1, 64)\n",
"\n",
" outputs = Conv2D(num_classes, 1, padding=\"same\", activation=\"softmax\")(d4)\n",
"\n",
" model = Model(inputs, outputs)\n",
" return model\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" input_shape = (512, 512, 3)\n",
" model = build_unet(input_shape, 11)\n",
" model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2025-01-15T16:40:48.430014Z",
"iopub.status.busy": "2025-01-15T16:40:48.429449Z",
"iopub.status.idle": "2025-01-15T21:46:53.922844Z",
"shell.execute_reply": "2025-01-15T21:46:53.921909Z",
"shell.execute_reply.started": "2025-01-15T16:40:48.429985Z"
},
"trusted": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train: 18168/18168 - Valid: 2000/2000 - Test: 2000/2000\n",
"\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8dee4daa95c34b6a8c673af5025c5f29",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"0epoch [00:00, ?epoch/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
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"text/plain": [
"0batch [00:00, ?batch/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 772ms/step - loss: 0.8056\n",
"Epoch 1: val_loss improved from inf to 0.32581, saving model to /kaggle/working/model.keras\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1896s\u001b[0m 803ms/step - loss: 0.8054 - val_loss: 0.3258 - learning_rate: 1.0000e-04\n",
"Epoch 2/10\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 775ms/step - loss: 0.2419\n",
"Epoch 2: val_loss improved from 0.32581 to 0.24630, saving model to /kaggle/working/model.keras\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1827s\u001b[0m 804ms/step - loss: 0.2418 - val_loss: 0.2463 - learning_rate: 1.0000e-04\n",
"Epoch 3/10\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 774ms/step - loss: 0.1912\n",
"Epoch 3: val_loss improved from 0.24630 to 0.21250, saving model to /kaggle/working/model.keras\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1826s\u001b[0m 804ms/step - loss: 0.1912 - val_loss: 0.2125 - learning_rate: 1.0000e-04\n",
"Epoch 4/10\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 775ms/step - loss: 0.1659\n",
"Epoch 4: val_loss improved from 0.21250 to 0.17604, saving model to /kaggle/working/model.keras\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1829s\u001b[0m 805ms/step - loss: 0.1659 - val_loss: 0.1760 - learning_rate: 1.0000e-04\n",
"Epoch 5/10\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 776ms/step - loss: 0.1521\n",
"Epoch 5: val_loss improved from 0.17604 to 0.16554, saving model to /kaggle/working/model.keras\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1830s\u001b[0m 806ms/step - loss: 0.1521 - val_loss: 0.1655 - learning_rate: 1.0000e-04\n",
"Epoch 6/10\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 776ms/step - loss: 0.1422\n",
"Epoch 6: val_loss improved from 0.16554 to 0.16303, saving model to /kaggle/working/model.keras\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1831s\u001b[0m 806ms/step - loss: 0.1422 - val_loss: 0.1630 - learning_rate: 1.0000e-04\n",
"Epoch 7/10\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 777ms/step - loss: 0.1324\n",
"Epoch 7: val_loss improved from 0.16303 to 0.15426, saving model to /kaggle/working/model.keras\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1832s\u001b[0m 807ms/step - loss: 0.1324 - val_loss: 0.1543 - learning_rate: 1.0000e-04\n",
"Epoch 8/10\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 777ms/step - loss: 0.1257\n",
"Epoch 8: val_loss improved from 0.15426 to 0.14793, saving model to /kaggle/working/model.keras\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1831s\u001b[0m 806ms/step - loss: 0.1257 - val_loss: 0.1479 - learning_rate: 1.0000e-04\n",
"Epoch 9/10\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 777ms/step - loss: 0.1170\n",
"Epoch 9: val_loss improved from 0.14793 to 0.14369, saving model to /kaggle/working/model.keras\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1832s\u001b[0m 807ms/step - loss: 0.1170 - val_loss: 0.1437 - learning_rate: 1.0000e-04\n",
"Epoch 10/10\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 777ms/step - loss: 0.1107\n",
"Epoch 10: val_loss did not improve from 0.14369\n",
"\u001b[1m2271/2271\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1830s\u001b[0m 806ms/step - loss: 0.1106 - val_loss: 0.1497 - learning_rate: 1.0000e-04\n"
]
}
],
"source": [
"\n",
"from tqdm.keras import TqdmCallback\n",
"global image_h\n",
"global image_w\n",
"global num_classes\n",
"global classes\n",
"global rgb_codes\n",
"\n",
"def create_dir(path):\n",
" if not os.path.exists(path):\n",
" os.makedirs(path)\n",
"\n",
"def load_dataset(path):\n",
" train_x = sorted(glob(os.path.join(path, \"train\", \"images\", \"*.jpg\")))\n",
" train_y = sorted(glob(os.path.join(path, \"train\", \"labels\", \"*.png\")))\n",
"\n",
" valid_x = sorted(glob(os.path.join(path, \"val\", \"images\", \"*.jpg\")))\n",
" valid_y = sorted(glob(os.path.join(path, \"val\", \"labels\", \"*.png\")))\n",
"\n",
" test_x = sorted(glob(os.path.join(path, \"test\", \"images\", \"*.jpg\")))\n",
" test_y = sorted(glob(os.path.join(path, \"test\", \"labels\", \"*.png\")))\n",
"\n",
" return (train_x, train_y), (valid_x, valid_y), (test_x, test_y)\n",
"\n",
"def read_image_mask(x, y):\n",
" \"\"\" Image \"\"\"\n",
" x = cv2.imread(x, cv2.IMREAD_COLOR)\n",
" x = cv2.resize(x, (image_w, image_h))\n",
" x = x/255.0\n",
" x = x.astype(np.float32)\n",
"\n",
" \"\"\" Mask \"\"\"\n",
" y = cv2.imread(y, cv2.IMREAD_GRAYSCALE)\n",
" y = cv2.resize(y, (image_w, image_h))\n",
" y = y.astype(np.int32)\n",
"\n",
" return x, y\n",
"\n",
"def preprocess(x, y):\n",
" def f(x, y):\n",
" x = x.decode()\n",
" y = y.decode()\n",
" return read_image_mask(x, y)\n",
"\n",
" image, mask = tf.numpy_function(f, [x, y], [tf.float32, tf.int32])\n",
" mask = tf.one_hot(mask, num_classes)\n",
"\n",
" image.set_shape([image_h, image_w, 3])\n",
" mask.set_shape([image_h, image_w, num_classes])\n",
"\n",
" return image, mask\n",
"\n",
"def tf_dataset(X, Y, batch=8):\n",
" ds = tf.data.Dataset.from_tensor_slices((X, Y))\n",
" ds = ds.shuffle(buffer_size=5000).map(preprocess)\n",
" ds = ds.batch(batch).prefetch(2)\n",
" return ds\n",
"\n",
"if __name__ == \"__main__\":\n",
" \"\"\" Seeding \"\"\"\n",
" np.random.seed(42)\n",
" tf.random.set_seed(42)\n",
"\n",
" \"\"\" Directory for storing files \"\"\"\n",
" create_dir(\"files\")\n",
"\n",
" \"\"\" Hyperparameters \"\"\"\n",
" image_h = 512\n",
" image_w = 512\n",
" num_classes = 11\n",
" input_shape = (image_h, image_w, 3)\n",
" batch_size = 8\n",
" lr = 1e-4 ## 0.0001\n",
" num_epochs = 10\n",
"\n",
" \"\"\" Paths \"\"\"\n",
" dataset_path = \"/kaggle/input/lapa-face-parsing-dataset/LaPa\"\n",
" model_path = \"/kaggle/working/model.keras\"\n",
" csv_path = \"/kaggle/working/data.csv\"\n",
"\n",
" \"\"\" RGB Code and Classes \"\"\"\n",
" rgb_codes = [\n",
" [0, 0, 0], [0, 153, 255], [102, 255, 153], [0, 204, 153],\n",
" [255, 255, 102], [255, 255, 204], [255, 153, 0], [255, 102, 255],\n",
" [102, 0, 51], [255, 204, 255], [255, 0, 102]\n",
" ]\n",
"\n",
" classes = [\n",
" \"background\", \"skin\", \"left eyebrow\", \"right eyebrow\",\n",
" \"left eye\", \"right eye\", \"nose\", \"upper lip\", \"inner mouth\",\n",
" \"lower lip\", \"hair\"\n",
" ]\n",
"\n",
" \"\"\" Loading the dataset \"\"\"\n",
" (train_x, train_y), (valid_x, valid_y), (test_x, test_y) = load_dataset(dataset_path)\n",
" print(f\"Train: {len(train_x)}/{len(train_y)} - Valid: {len(valid_x)}/{len(valid_y)} - Test: {len(test_x)}/{len(test_x)}\")\n",
" print(\"\")\n",
"\n",
" \"\"\" Dataset Pipeline \"\"\"\n",
" train_ds = tf_dataset(train_x, train_y, batch=batch_size)\n",
" valid_ds = tf_dataset(valid_x, valid_y, batch=batch_size)\n",
"\n",
" \"\"\" Model \"\"\"\n",
" model = build_unet(input_shape, num_classes)\n",
" model.compile(\n",
" loss=\"categorical_crossentropy\",\n",
" optimizer=tf.keras.optimizers.Adam(lr)\n",
" )\n",
"\n",
" \"\"\" Training \"\"\"\n",
" callbacks = [\n",
" TqdmCallback(verbose=1),\n",
" ModelCheckpoint(model_path, verbose=1, save_best_only=True, monitor='val_loss'),\n",
" ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=1e-7, verbose=1),\n",
" CSVLogger(csv_path, append=True),\n",
" EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=False)\n",
" ]\n",
"\n",
" model.fit(train_ds,\n",
" validation_data=valid_ds,\n",
" epochs=num_epochs,\n",
" callbacks=callbacks\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
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},
"jupyter": {
"source_hidden": true
},
"trusted": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The directory /kaggle/working/output does not exist.\n"
]
}
],
"source": [
"# import shutil\n",
"\n",
"# # Path to the Kaggle output directory\n",
"# output_dir = \"/kaggle/working/output\"\n",
"\n",
"# try:\n",
"# # Delete the directory and its contents\n",
"# shutil.rmtree(output_dir)\n",
"# print(f\"Successfully deleted the directory: {output_dir}\")\n",
"# except FileNotFoundError:\n",
"# print(f\"The directory {output_dir} does not exist.\")\n",
"# except PermissionError:\n",
"# print(f\"Permission denied to delete the directory: {output_dir}\")\n",
"# except Exception as e:\n",
"# print(f\"An error occurred: {e}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2025-01-18T12:55:04.971403Z",
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"shell.execute_reply.started": "2025-01-18T12:55:04.971379Z"
},
"trusted": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"import cv2\n",
"import matplotlib.pyplot as plt\n",
"import imutils\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2025-02-11T14:54:49.075579Z",
"iopub.status.busy": "2025-02-11T14:54:49.075283Z",
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"shell.execute_reply.started": "2025-02-11T14:54:49.075559Z"
},
"trusted": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train: 18168/18168 - Valid: 2000/2000 - Test: 2000/2000\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 2000/2000 [18:47<00:00, 1.77it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Class F1 Jaccard \n",
"-----------------------------------\n",
"background : 0.97259 - 0.94849\n",
"skin : 0.94535 - 0.89980\n",
"left eyebrow : 0.55470 - 0.41518\n",
"right eyebrow : 0.60111 - 0.46519\n",
"left eye : 0.55648 - 0.41848\n",
"right eye : 0.56165 - 0.42519\n",
"nose : 0.91613 - 0.85186\n",
"upper lip : 0.69397 - 0.54920\n",
"inner mouth : 0.49528 - 0.39525\n",
"lower lip : 0.72589 - 0.59049\n",
"hair : 0.86952 - 0.79818\n",
"-----------------------------------\n",
"Mean : 0.71751 - 0.61430\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"\n",
"import os\n",
"\n",
"import numpy as np\n",
"import cv2\n",
"import pandas as pd\n",
"from glob import glob\n",
"from tqdm import tqdm\n",
"import tensorflow as tf\n",
"from sklearn.metrics import f1_score, jaccard_score\n",
"\n",
"global image_h\n",
"global image_w\n",
"global num_classes\n",
"global classes\n",
"global rgb_codes\n",
"\n",
"\n",
"def load_dataset(path):\n",
" train_x = sorted(glob(os.path.join(path, \"train\", \"images\", \"*.jpg\")))\n",
" train_y = sorted(glob(os.path.join(path, \"train\", \"labels\", \"*.png\")))\n",
"\n",
" valid_x = sorted(glob(os.path.join(path, \"val\", \"images\", \"*.jpg\")))\n",
" valid_y = sorted(glob(os.path.join(path, \"val\", \"labels\", \"*.png\")))\n",
"\n",
" test_x = sorted(glob(os.path.join(path, \"test\", \"images\", \"*.jpg\")))\n",
" test_y = sorted(glob(os.path.join(path, \"test\", \"labels\", \"*.png\")))\n",
"\n",
" return (train_x, train_y), (valid_x, valid_y), (test_x, test_y)\n",
"\n",
"def grayscale_to_rgb(mask, rgb_codes):\n",
" h, w = mask.shape[0], mask.shape[1]\n",
" mask = mask.astype(np.int32)\n",
" output = []\n",
"\n",
" for i, pixel in enumerate(mask.flatten()):\n",
" output.append(rgb_codes[pixel])\n",
"\n",
" output = np.reshape(output, (h, w, 3))\n",
" return output\n",
"\n",
"def save_results(image_x, mask, pred, save_image_path):\n",
" mask = np.expand_dims(mask, axis=-1)\n",
" mask = grayscale_to_rgb(mask, rgb_codes)\n",
"\n",
" pred = np.expand_dims(pred, axis=-1)\n",
" pred = grayscale_to_rgb(pred, rgb_codes)\n",
"\n",
" line = np.ones((image_x.shape[0], 10, 3)) * 255\n",
"\n",
" cat_images = np.concatenate([image_x, line, mask, line, pred], axis=1)\n",
" cv2.imwrite(save_image_path, cat_images)\n",
"\n",
"if __name__ == \"__main__\":\n",
" \"\"\" Seeding \"\"\"\n",
" np.random.seed(42)\n",
" tf.random.set_seed(42)\n",
"\n",
"\n",
" \"\"\" Hyperparameters \"\"\"\n",
" image_h = 512\n",
" image_w = 512\n",
" num_classes = 11\n",
"\n",
" \"\"\" Paths \"\"\"\n",
" dataset_path = \"/kaggle/input/lapa-face-parsing-dataset/LaPa\"\n",
" model_path = \"/kaggle/input/trained-model/10epoch model.h5\"\n",
"\n",
" \"\"\" RGB Code and Classes \"\"\"\n",
" rgb_codes = [\n",
" [0, 0, 0], [0, 153, 255], [102, 255, 153], [0, 204, 153],\n",
" [255, 255, 102], [255, 255, 204], [255, 153, 0], [255, 102, 255],\n",
" [102, 0, 51], [255, 204, 255], [255, 0, 102]\n",
" ]\n",
"\n",
" classes = [\n",
" \"background\", \"skin\", \"left eyebrow\", \"right eyebrow\",\n",
" \"left eye\", \"right eye\", \"nose\", \"upper lip\", \"inner mouth\",\n",
" \"lower lip\", \"hair\"\n",
" ]\n",
"\n",
" \"\"\" Loading the dataset \"\"\"\n",
" (train_x, train_y), (valid_x, valid_y), (test_x, test_y) = load_dataset(dataset_path)\n",
" print(f\"Train: {len(train_x)}/{len(train_y)} - Valid: {len(valid_x)}/{len(valid_y)} - Test: {len(test_x)}/{len(test_x)}\")\n",
" print(\"\")\n",
"\n",
" \"\"\" Load the model \"\"\"\n",
" model = tf.keras.models.load_model(model_path)\n",
"\n",
" \"\"\" Prediction & Evaluation \"\"\"\n",
" SCORE = []\n",
" for x, y in tqdm(zip(test_x, test_y), total=len(test_x)):\n",
" \"\"\" Extract the name \"\"\"\n",
" name = x.split(\"/\")[-1].split(\".\")[0]\n",
"\n",
" \"\"\" Reading the image \"\"\"\n",
" image = cv2.imread(x, cv2.IMREAD_COLOR)\n",
" image = cv2.resize(image, (image_w, image_h))\n",
" image_x = image\n",
" image = image/255.0 ## (H, W, 3)\n",
" image = np.expand_dims(image, axis=0) ## [1, H, W, 3]\n",
" image = image.astype(np.float32)\n",
"\n",
" \"\"\" Reading the mask \"\"\"\n",
" mask = cv2.imread(y, cv2.IMREAD_GRAYSCALE)\n",
" mask = cv2.resize(mask, (image_w, image_h))\n",
" mask = mask.astype(np.int32)\n",
"\n",
" \"\"\" Prediction \"\"\"\n",
" pred = model.predict(image, verbose=0)[0]\n",
" pred = np.argmax(pred, axis=-1) ## [0.1, 0.2, 0.1, 0.6] -> 3\n",
" pred = pred.astype(np.int32)\n",
"\n",
" ## cv2.imwrite(\"pred.png\", pred * (255/11))\n",
"\n",
" # rgb_mask = grayscale_to_rgb(pred, rgb_codes)\n",
" # cv2.imwrite(\"pred.png\", rgb_mask)\n",
"\n",
" \"\"\" Save the results \"\"\"\n",
" save_image_path = f\"results/{name}.png\"\n",
" save_results(image_x, mask, pred, save_image_path)\n",
"\n",
" \"\"\" Flatten the array \"\"\"\n",
" mask = mask.flatten()\n",
" pred = pred.flatten()\n",
"\n",
" labels = [i for i in range(num_classes)]\n",
"\n",
" \"\"\" Calculating the metrics values \"\"\"\n",
" f1_value = f1_score(mask, pred, labels=labels, average=None, zero_division=0)\n",
" jac_value = jaccard_score(mask, pred, labels=labels, average=None, zero_division=0)\n",
"\n",
" SCORE.append([f1_value, jac_value])\n",
"\n",
" score = np.array(SCORE)\n",
" score = np.mean(score, axis=0)\n",
"\n",
" f = open(\"/kaggle/working/scores.csv\", \"w\")\n",
" f.write(\"Class,F1,Jaccard\\n\")\n",
"\n",
" l = [\"Class\", \"F1\", \"Jaccard\"]\n",
" print(f\"{l[0]:15s} {l[1]:10s} {l[2]:10s}\")\n",
" print(\"-\"*35)\n",
"\n",
" for i in range(num_classes):\n",
" class_name = classes[i]\n",
" f1 = score[0, i]\n",
" jac = score[1, i]\n",
" dstr = f\"{class_name:15s}: {f1:1.5f} - {jac:1.5f}\"\n",
" print(dstr)\n",
" f.write(f\"{class_name:15s},{f1:1.5f},{jac:1.5f}\\n\")\n",
"\n",
" print(\"-\"*35)\n",
" class_mean = np.mean(score, axis=-1)\n",
" class_name = \"Mean\"\n",
"\n",
" f1 = class_mean[0]\n",
" jac = class_mean[1]\n",
"\n",
" dstr = f\"{class_name:15s}: {f1:1.5f} - {jac:1.5f}\"\n",
" print(dstr)\n",
" f.write(f\"{class_name:15s},{f1:1.5f},{jac:1.5f}\\n\")\n",
"\n",
" f.close()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2025-02-11T14:54:17.543580Z",
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"trusted": true
},
"outputs": [],
"source": [
"dfq = pd.DataFrame(list())\n",
"dfq.to_csv('scores.csv')"
]
}
],
"metadata": {
"kaggle": {
"accelerator": "gpu",
"dataSources": [
{
"datasetId": 4705085,
"sourceId": 7992059,
"sourceType": "datasetVersion"
},
{
"datasetId": 6491422,
"sourceId": 10484428,
"sourceType": "datasetVersion"
}
],
"dockerImageVersionId": 30839,
"isGpuEnabled": true,
"isInternetEnabled": true,
"language": "python",
"sourceType": "notebook"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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|