Row 4015

Row ID: 4015 | Dataset Entry | Axioma AXP Content Repository

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import tensorflow as tf from tensorflow.keras import layers, models, optimizers from tensorflow.keras.preprocessing.image import ImageDataGenerator

class BottleneckBlock(tf.keras.layers.Layer): def __init__(self, in_channels, out_channels, stride=1): super(BottleneckBlock, self).__init__() self.conv1 = tf.keras.layers.Conv2D(out_channels, kernel_size=1, strides=stride, padding='same', use_bias=False) self.bn1 = tf.keras.layers.BatchNormalization() self.relu = tf.keras.layers.ReLU() self.conv2 = tf.keras.layers.Conv2D(out_channels, kernel_size=3, strides=1, padding='same', use_bias=False) self.bn2 = tf.keras.layers.BatchNormalization() self.conv3 = tf.keras.layers.Conv2D(out_channels * 4, kernel_size=1, strides=1, padding='same', use_bias=False) self.bn3 = tf.keras.layers.BatchNormalization() self.downsample = tf.keras.Sequential([ tf.keras.layers.Conv2D(out_channels * 4, kernel_size=1, strides=stride, use_bias=False), tf.keras.layers.BatchNormalization() ]) if stride != 1 else None self.stride = stride

def call(self, x): identity = x

out = self.conv1(x) out = self.bn1(out) out = self.relu(out)

out = self.conv2(out) out = self.bn2(out) out = self.relu(out)

out = self.conv3(out) out = self.bn3(out)

if self.downsample is not None: identity = self.downsample(x)

out += identity out = self.relu(out)

return out

class CNN(models.Model): def __init__(self, block, layers, num_classes=10): super(CNN, self).__init__() self.in_channels = 64 self.conv1 = tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, padding='same', use_bias=False) self.bn1 = tf.keras.layers.BatchNormalization() self.relu = tf.keras.layers.ReLU() self.maxpool = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=2, padding='same') self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2) self.layer3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) self.avgpool = tf.keras.layers.GlobalAveragePooling2D() self.fc = tf.keras.layers.Dense(num_classes)

def _make_layer(self, block, out_channels, blocks, stride=1): layers = [] layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * 4 # block.expansion = 4 for _ in range(1, blocks): layers.append(block(self.in_channels, out_channels)) return tf.keras.Sequential(layers)

def call(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x)

x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x)

x = self.avgpool(x) x = self.fc(x) return x

def ResNet50(num_classes=10): return CNN(BottleneckBlock, [3, 4, 6, 3], num_classes)

# Dataset and DataGenerator train_datagen = ImageDataGenerator( rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)

train_generator = train_datagen.flow_from_directory( '/kaggle/input/prostate-cancer', target_size=(224, 224), batch_size=32, class_mode='categorical')

# Example usage model = ResNet50(num_classes=len(train_generator.class_indices)) model.compile(optimizer=optimizers.Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy']) model.fit(train_generator, epochs=10)

def predict_image_class(image_path): img = tf.keras.preprocessing.image.load_img(image_path, target_size=(224, 224)) img_array = tf.keras.preprocessing.image.img_to_array(img) img_array = tf.expand_dims(img_array, 0) # Create batch axis img_array /= 255. # Normalize predicted_class = model.predict(img_array) return tf.argmax(predicted_class[0]).numpy()

# Example usage image_path = '/kaggle/input/predict-img/0001.png' predicted_class = predict_image_class(image_path) print(f"Predicted class: {predicted_class}")

FieldValue
text import tensorflow as tf from tensorflow.keras import layers, models, optimizers from tensorflow.keras.preprocessing.image import ImageDataGenerator class BottleneckBlock(tf.keras.layers.Layer): def __init__(self, in_channels, out_channels, stride=1): super(BottleneckBlock, self).__init__() self.conv1 = tf.keras.layers.Conv2D(out_channels, kernel_size=1, strides=stride, padding='same', use_bias=False) self.bn1 = tf.keras.layers.BatchNormalization() self.relu =…
label r/neuralnetworks
dataType post
communityName r/neuralnetworks
datetime 2024-04-01
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Raw Record

{
  "text": "import tensorflow as tf\nfrom tensorflow.keras import layers, models, optimizers\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\n\nclass BottleneckBlock(tf.keras.layers.Layer):\n    def __init__(self, in_channels, out_channels, stride=1):\n        super(BottleneckBlock, self).__init__()\n        self.conv1 = tf.keras.layers.Conv2D(out_channels, kernel_size=1, strides=stride, padding='same', use_bias=False)\n        self.bn1 = tf.keras.layers.BatchNormalization()\n        self.relu = tf.keras.layers.ReLU()\n        self.conv2 = tf.keras.layers.Conv2D(out_channels, kernel_size=3, strides=1, padding='same', use_bias=False)\n        self.bn2 = tf.keras.layers.BatchNormalization()\n        self.conv3 = tf.keras.layers.Conv2D(out_channels * 4, kernel_size=1, strides=1, padding='same', use_bias=False)\n        self.bn3 = tf.keras.layers.BatchNormalization()\n        self.downsample = tf.keras.Sequential([\n            tf.keras.layers.Conv2D(out_channels * 4, kernel_size=1, strides=stride, use_bias=False),\n            tf.keras.layers.BatchNormalization()\n        ]) if stride != 1 else None\n        self.stride = stride\n\n    def call(self, x):\n        identity = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            identity = self.downsample(x)\n\n        out += identity\n        out = self.relu(out)\n\n        return out\n\nclass CNN(models.Model):\n    def __init__(self, block, layers, num_classes=10):\n        super(CNN, self).__init__()\n        self.in_channels = 64\n        self.conv1 = tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, padding='same', use_bias=False)\n        self.bn1 = tf.keras.layers.BatchNormalization()\n        self.relu = tf.keras.layers.ReLU()\n        self.maxpool = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=2, padding='same')\n        self.layer1 = self._make_layer(block, 64, layers[0])\n        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n        self.avgpool = tf.keras.layers.GlobalAveragePooling2D()\n        self.fc = tf.keras.layers.Dense(num_classes)\n\n    def _make_layer(self, block, out_channels, blocks, stride=1):\n        layers = []\n        layers.append(block(self.in_channels, out_channels, stride))\n        self.in_channels = out_channels * 4  # block.expansion = 4\n        for _ in range(1, blocks):\n            layers.append(block(self.in_channels, out_channels))\n        return tf.keras.Sequential(layers)\n\n    def call(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        x = self.maxpool(x)\n\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        x = self.avgpool(x)\n        x = self.fc(x)\n        return x\n\ndef ResNet50(num_classes=10):\n    return CNN(BottleneckBlock, [3, 4, 6, 3], num_classes)\n\n# Dataset and DataGenerator\ntrain_datagen = ImageDataGenerator(\n    rescale=1./255,\n    shear_range=0.2,\n    zoom_range=0.2,\n    horizontal_flip=True)\n\ntrain_generator = train_datagen.flow_from_directory(\n    '/kaggle/input/prostate-cancer',\n    target_size=(224, 224),\n    batch_size=32,\n    class_mode='categorical')\n\n# Example usage\nmodel = ResNet50(num_classes=len(train_generator.class_indices))\nmodel.compile(optimizer=optimizers.Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\nmodel.fit(train_generator, epochs=10)\n\ndef predict_image_class(image_path):\n    img = tf.keras.preprocessing.image.load_img(image_path, target_size=(224, 224))\n    img_array = tf.keras.preprocessing.image.img_to_array(img)\n    img_array = tf.expand_dims(img_array, 0)  # Create batch axis\n    img_array /= 255.  # Normalize\n    predicted_class = model.predict(img_array)\n    return tf.argmax(predicted_class[0]).numpy()\n\n# Example usage\nimage_path = '/kaggle/input/predict-img/0001.png'\npredicted_class = predict_image_class(image_path)\nprint(f\"Predicted class: {predicted_class}\")\n",
  "label": "r/neuralnetworks",
  "dataType": "post",
  "communityName": "r/neuralnetworks",
  "datetime": "2024-04-01",
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}
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