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Puranjay Savar Mattas
2022-09-03 23:17:04 +05:30
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ShipsSatelliteImageClassification/main.ipynb
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"source": [
"#### Explanation of features\n",
"\n",
"1. Conv2D - This is a 2 dimensional convolutional layer, the number of filters decide what the convolutional layer learns. Greater the number of filters, greater the amount of information obtained. <img src=\"/assets/img/MarineGEO_logo.png\" alt=\"MarineGEO circle logo\" style=\"height: 100px; width:100px;\"/>\n",
"1. Conv2D - This is a 2 dimensional convolutional layer, the number of filters decide what the convolutional layer learns. Greater the number of filters, greater the amount of information obtained. <img src=\"https://github.com/psavarmattas/Machine-Learning-Models/blob/2308384eeb0e7e5b6e16a9d76698a59bc08b9bff/ShipsSatelliteImageClassification/assets/keras_conv2d_num_filters.png\" alt=\"MarineGEO circle logo\" style=\"height: 100px; width:100px;\"/>\n",
"2. MaxPooling2D - This reduces the spatial dimensions of the feature map produced by the convolutional layer without losing any range information. This allows a model to become slightly more robust\n",
"3. Dropout - This removes a user-defined percentage of links between neurons of consecutive layers. This allows the model to be robust. It can be used in both fully convolutional layers and fully connected layers.\n",
"4. BatchNormalization - This layer normalises the values present in the hidden part of the neural network. This is similar to MinMax/Standard scaling applied in machine learning algorithms\n",