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    "info": {
        "author": "Mohamed Abdou",
        "author_email": "matex512@gmail.com",
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            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python :: 3"
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        "description": "# Flexible_Neural_Net\nA simple and flexible python library that allows you to build custom Neural Networks where you can easily tweak parameters to change how your network behaves\n\n## Installation\n```pip install flexible-neural-network```\n\n## Initialization\n* First initialize a Neural Net object and pass number of inputs, outputs, and hidden layers\n\n  ```myNN = NeuralNet(number_of_inputs, number_of_outputs, number_of_hidden_layers)```\n* You can choose how what _activation function_ to use from: \"relu\", \"sigmoid, \"tanh\"\n\n  ```myNN = NeuralNet(number_of_inputs, number_of_outputs, number_of_hidden_layers, activation_func=\"sigmoid\")```\n* You can choose modify the _learning rate_\n\n  ```myNN = NeuralNet(number_of_inputs, number_of_outputs, number_of_hidden_layers, learning_rate=0.1)```\n* You can choose tweak the number of nodes in each _hidden layer_\n\n  *   by assigning an integer number such as 3: _if there was 4 hidden layers then each layer will have 3 nodes => [3, 3, 3, 3]_\n\n      ```myNN = NeuralNet(number_of_inputs, number_of_outputs, number_of_hidden_layers, nodes_in_each_layer=3)```\n\n  *   by assigning a list of integers number such as [3, 5, 2, 3] that has a length of *number_of_hidden_layers*: _if there was 4 hidden layers then each layer will have different number of nodes nodes correspondingly => [3, 5, 2, 3]_\n\n      ```myNN = NeuralNet(number_of_inputs, number_of_outputs, number_of_hidden_layers, nodes_in_each_layer=[3, 5, 2, 3])```\n\n\n## How to use\n\n#### Assuming you initialized your object and data as below:\n\n```myNN = NeuralNet(2, 1, 2, nodes_in_each_layer=4, learning_rate=0.1, activation_func=\"sigmoid\")```\n```\ndata = np.array([\n        [3,   1.5, 1],\n        [2,   1,   0],\n        [4,   1.5, 1],\n        [3,   1,   0],\n        [3.5, .5,  1],\n        [2,   .5,  0],\n        [5.5,  1,  1],\n        [1,    1,  0]\n        ])\n\nmystery_data = [2, 1] # should be classified as 1\n```\n\n#### You can:\n\n_Here we specified the number of epochs to be 1_\n* *Train single entries:*  \n  ```myNN.train(data[0, 0:2], data[0, 2], epochs=1)```\n\n* *Train multiple entries*\n  ```myNN.train_many(data[:, 0:2], data[:, 2], epochs=1)```\n\n* *test single/multiple entries*\n  ```output = myNN.test(mystery_flower)```\n  where output is always an np.ndarray with size as the specfied in the object's constructor. _for the current example it's = [1.45327823]_\n\n* *Save NN for later*\n  ```myNN.save(\"file_name\")```\n\n* *Load NN without the need for retraining*\n  ```myNN = NeuralNet.load(\"file_name\")```\n\nObviously NNs **do not** give exact answers and its our job to determine which class is it belongs to and judging from the training data we only have class 0, or 1 and the output we got is nearer to 1 than 0 so we should **classify it as  1**\n\n\n",
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