Student_class.ipynb

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      "source": [
        "# ------------------------------------------------------\n",
        "# Sierra Janson\n",
        "# Last Updated: 04/10/2024\n",
        "# Example of Student Class\n",
        "\n",
        "# Questions to consider:\n",
        "# Should there be a default activation function/ hidden neuron number?\n",
        "# Should we add an id parameter to Student class for identification purposes?\n",
        "# How much leniency do we give user? (Error handling, default values)\n",
        "# Should the randomization of the of the number of neurons be handled by user or class?\n",
        "\n",
        "# ------------------------------------------------------\n",
        "import numpy as np\n",
        "from typing import Callable\n",
        "\n",
        "\n",
        "# ------------------------------------------------------\n",
        "class Student():\n",
        "    def __init__(self, id: int, activation_function: Callable, act_func_deriv: Callable, total_weights: int): # hn user specified .. upper limit (20...1000) <-- testing\n",
        "        self.id = id\n",
        "        self.activation_function = activation_function\n",
        "        self.act_func_deriv = act_func_deriv\n",
        "        self.total_weights = total_weights\n",
        "        self.hidden_weights = None\n",
        "        self.outer_weights = None\n",
        "\n",
        "    # make sure no ids are duplicated\n",
        "    def set_hidden_weights(self, weights: list):\n",
        "        self.hidden_weights = weights\n",
        "\n",
        "    def set_outer_weights(self, weights: list):\n",
        "        self.outer_weights = weights\n",
        "\n",
        "    def get_hidden_weights(self) -> list:\n",
        "        return self.hidden_weights\n",
        "\n",
        "    def get_outer_weights(self) -> list:\n",
        "        return self.outer_weights\n",
        "\n",
        "    def evaluate(self, input: float) -> float:\n",
        "        return self.y_0(self.hidden_layer(x))\n",
        "\n",
        "    # GET/SET functions for neurons and activation function -----------------------------\n",
        "    def set_total_weights(self, total_weights: int):\n",
        "        # EXPERIMENT with upper limit\n",
        "        if total_weights > 20:\n",
        "          print(\"An ANN cannot have too many weights (too many neurons).\")\n",
        "        elif total_weights > 0:\n",
        "          self.total_weights = total_weights\n",
        "        else:\n",
        "          print(\"An ANN cannot have a negative number of hidden weights.\")\n",
        "\n",
        "    def get_total_weights(self) -> int:\n",
        "        return self.total_weights\n",
        "\n",
        "    def set_activation_function(self, activation_function: Callable):\n",
        "        self.activation_function = activation_function\n",
        "\n",
        "    def get_activation_function(self) -> Callable:\n",
        "        return self.activation_function\n",
        "\n",
        "    def y_0(self, hidden_layer: list):\n",
        "      return self.activation_function(sum(hidden_layer))\n",
        "\n",
        "    def z(self,wi,x):\n",
        "      return self.activation_function(wi*x)\n",
        "\n",
        "    def hidden_layer(self,x):\n",
        "      hidden_layer = []\n",
        "      for i in range(10):\n",
        "        hidden_layer.append(self.z(self.hidden_weights[i],x) * self.outer_weights[i])\n",
        "      return hidden_layer\n",
        "\n",
        "    def deriv_loss(self, y_0, y):\n",
        "      return (y_0 - y)\n",
        "\n",
        "    def deriv_hidden_weight(self, x, y, y_0, w_index):\n",
        "      return self.deriv_loss(y_0,y) * self.act_func_deriv(sum(self.hidden_layer(x))) * self.outer_weights[w_index] * self.act_func_deriv(self.hidden_weights[w_index]*x) * x\n",
        "\n",
        "    def deriv_outer_weight(self, x, y, y_0, w_index):\n",
        "      return self.deriv_loss(y_0,y) * self.act_func_deriv(sum(self.hidden_layer(x))) * self.z(self.hidden_weights[w_index], x)\n",
        "\n",
        "    def train(self, x_arr, η, epochs):\n",
        "      for i in range(len(x_arr)):\n",
        "        x = x_arr[i]\n",
        "        y = y_arr[i]\n",
        "        print(\"Training with (x,y) to be (\" + str(x) + \",\" + str(y) + \").\")\n",
        "        print(\"Learning rate: \", η)\n",
        "\n",
        "        print(\"Epoch \", end=\"\")\n",
        "        for j in range(epochs):\n",
        "          print(\".\", end=\"\")\n",
        "          y_0 = self.evaluate(x)\n",
        "\n",
        "          # updating hidden layer\n",
        "          for i in range(self.total_weights//2):\n",
        "            self.hidden_weights[i] = self.hidden_weights[i] - η * self.deriv_hidden_weight(x, y, y_0, i)\n",
        "\n",
        "          # updating outer layer\n",
        "          for i in range(self.total_weights//2):\n",
        "            self.outer_weights[i] = self.outer_weights[i] - η * self.deriv_outer_weight(x, y, y_0, i)\n",
        "        print()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SOME ACTIVATION FUNCTIONS (with sources cited if you want to find out how it works) ------------------------------------------------------------\n",
        "# pass parameter\n",
        "def sigmoid(x):                                     # source: https://www.geeksforgeeks.org/implement-sigmoid-function-using-numpy/\n",
        "    # all real numbers -> [0,1]\n",
        "    return 1/(1 + np.exp(-x))\n",
        "def relu(x):                                        # source: https://www.digitalocean.com/community/tutorials/relu-function-in-python\n",
        "    return max(0.0, x)\n",
        "def tanh(x):\n",
        "    return np.tanh(x)\n",
        "def elu(x, alpha):                                  # source: https://paperswithcode.com/method/elu#:~:text=The%20Exponential%20Linear%20Unit%20(ELU)%20is%20an%20activation%20function%20for,but%20with%20lower%20computational%20complexity.\n",
        "    if x > 0: return x\n",
        "    else:\n",
        "      assert(alpha > 0)\n",
        "      return alpha*(np.exp(x)-1)\n",
        "def softmax(x):                                     # source: https://machinelearningmastery.com/softmax-activation-function-with-python/\n",
        "    return np.exp(x) / np.exp(x).sum()\n",
        "\n",
        "# source: Josh Barsky's Notebook LINK!\n",
        "η = .1\n",
        "ε = 0.01\n",
        "\n",
        "def Γ(x):\n",
        "  if x < 0:\n",
        "    return ε*x\n",
        "  else:\n",
        "    return x\n",
        "\n",
        "def Γ_deriv(x):\n",
        "  if x < 0:\n",
        "    return ε\n",
        "  else:\n",
        "    return 1"
      ],
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      "source": [
        "if (__name__ == \"__main__\"):\n",
        "    # Initializing Student\n",
        "    act_func = Γ                                         # pass activation function\n",
        "    act_func_deriv = Γ_deriv\n",
        "    neurons_per_layer = 10\n",
        "    student = Student(1, act_func, act_func_deriv, neurons_per_layer*2)\n",
        "\n",
        "\n",
        "    # Setting Weights\n",
        "    hidden_layer_weights = []\n",
        "    out_weights = []\n",
        "\n",
        "    for i in range(neurons_per_layer):\n",
        "      # layer one weights\n",
        "      hidden_layer_weights.append(.5)\n",
        "      # layer two weights\n",
        "      out_weights.append(.5)\n",
        "\n",
        "    student.set_hidden_weights(hidden_layer_weights)\n",
        "    student.set_outer_weights(out_weights)\n",
        "\n",
        "    print(\"Beginning values:\")\n",
        "    print()\n",
        "\n",
        "    print(\"Hidden Layer Weights: \")\n",
        "    for i in range(neurons_per_layer):\n",
        "      print(f\"Weight w{i}     : \", student.get_hidden_weights()[i])\n",
        "    print()\n",
        "\n",
        "    print(\"Outer Layer Weights: \")\n",
        "    for i in range(neurons_per_layer):\n",
        "      print(f\"Weight w{i}     : \", student.get_outer_weights()[i])\n",
        "    print()\n",
        "\n",
        "    # Training Constants\n",
        "    # Define epochs for all tests\n",
        "    epochs = 10000\n",
        "\n",
        "    # We let (x,y) be the following values.\n",
        "    x_arr = np.arange(1, 6)\n",
        "    y_arr = x_arr*5\n",
        "\n",
        "    for i in range(len(x_arr)):\n",
        "      x = x_arr[i]\n",
        "      print(\"Input (x)    : \", x, end = \" --> \")\n",
        "      print(\"Output (y_0) : \", student.evaluate(x))\n",
        "\n",
        "    print(\"\\nStarting training...\")\n",
        "\n",
        "    student.train(x_arr, η, epochs)"
      ],
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          "name": "stdout",
          "text": [
            "Beginning values:\n",
            "\n",
            "Hidden Layer Weights: \n",
            "Weight w0     :  0.5\n",
            "Weight w1     :  0.5\n",
            "Weight w2     :  0.5\n",
            "Weight w3     :  0.5\n",
            "Weight w4     :  0.5\n",
            "Weight w5     :  0.5\n",
            "Weight w6     :  0.5\n",
            "Weight w7     :  0.5\n",
            "Weight w8     :  0.5\n",
            "Weight w9     :  0.5\n",
            "\n",
            "Outer Layer Weights: \n",
            "Weight w0     :  0.5\n",
            "Weight w1     :  0.5\n",
            "Weight w2     :  0.5\n",
            "Weight w3     :  0.5\n",
            "Weight w4     :  0.5\n",
            "Weight w5     :  0.5\n",
            "Weight w6     :  0.5\n",
            "Weight w7     :  0.5\n",
            "Weight w8     :  0.5\n",
            "Weight w9     :  0.5\n",
            "\n",
            "Input (x)    :  1 --> Output (y_0) :  2.5\n",
            "Input (x)    :  2 --> Output (y_0) :  5.0\n",
            "Input (x)    :  3 --> Output (y_0) :  7.5\n",
            "Input (x)    :  4 --> Output (y_0) :  10.0\n",
            "Input (x)    :  5 --> Output (y_0) :  12.5\n",
            "\n",
            "Starting training...\n",
            "Training with (x,y) to be (1,5).\n",
            "Learning rate:  0.1\n",
            "Epoch ........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................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\n",
            "Training with (x,y) to be (2,10).\n",
            "Learning rate:  0.1\n",
            "Epoch ........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................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\n",
            "Training with (x,y) to be (3,15).\n",
            "Learning rate:  0.1\n",
            "Epoch ........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................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\n",
            "Training with (x,y) to be (4,20).\n",
            "Learning rate:  0.1\n",
            "Epoch ........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................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\n",
            "Training with (x,y) to be (5,25).\n",
            "Learning rate:  0.1\n",
            "Epoch ........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................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\n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [
        "  # The following is the program version of the described neural network above, abstracted in functions\n",
        "\n",
        "def lecture_learning_training(hidden_neurons):\n",
        "    hidden_layer_weights = []\n",
        "    out_weights = []\n",
        "\n",
        "\n",
        "    for i in range(hidden_neurons):\n",
        "      # layer one weights\n",
        "      hidden_layer_weights.append(.5)\n",
        "      # layer two weights\n",
        "      out_weights.append(.5)\n",
        "\n",
        "    η = .1\n",
        "\n",
        "    # For leaky relu function\n",
        "    ε = 0.01\n",
        "\n",
        "    # Main functions\n",
        "    def Γ(x):\n",
        "      if x < 0:\n",
        "        return ε*x\n",
        "      else:\n",
        "        return x\n",
        "\n",
        "    def f(z_list):\n",
        "      return Γ(sum(z_list))\n",
        "\n",
        "    def z_list(x):\n",
        "      z_list = []\n",
        "      for i in range(hidden_neurons):\n",
        "        z_list.append(z(hidden_layer_weights[i],x) * out_weights[i])\n",
        "      return z_list\n",
        "\n",
        "    def z(wi,x):\n",
        "      return Γ(wi*x)\n",
        "\n",
        "    # Derivatives\n",
        "    def Γ_deriv(x):\n",
        "      if x < 0:\n",
        "        return ε\n",
        "      else:\n",
        "        return 1\n",
        "\n",
        "    def deriv_loss(y_0):\n",
        "      return (y_0 - y)\n",
        "\n",
        "    def deriv_hidden_weight(y_0, w_index):\n",
        "      return deriv_loss(y_0) * Γ_deriv(sum(z_list(x))) * out_weights[w_index] * Γ_deriv(hidden_layer_weights[w_index]*x) * x\n",
        "\n",
        "    def deriv_outer_weight(y_0, w_index):\n",
        "      return deriv_loss(y_0) * Γ_deriv(sum(z_list(x))) * z(hidden_layer_weights[w_index], x)\n",
        "\n",
        "    print(\"Beginning values:\")\n",
        "    print(\"Hidden Layer Weights: \")\n",
        "    for i in range(hidden_neurons):\n",
        "      print(f\"Weight w{i}     : \", hidden_layer_weights[i])\n",
        "\n",
        "    print(\"Outer Layer Weights: \")\n",
        "    for i in range(hidden_neurons):\n",
        "      print(f\"Weight w{i}     : \", hidden_layer_weights[i])\n",
        "\n",
        "\n",
        "    for i in range(len(x_arr)):\n",
        "      x = x_arr[i]\n",
        "      print(\"Input (x)    : \", x, end = \" --> \")\n",
        "      print(\"Output (y_0) : \", f(z_list(x)))\n",
        "\n",
        "    print(\"\\nStarting training...\")\n",
        "\n",
        "    for i in range(len(x_arr)):\n",
        "      x = x_arr[i]\n",
        "      y = y_arr[i]\n",
        "      print(\"Training with (x,y) to be (\" + str(x) + \",\" + str(y) + \").\")\n",
        "      print(\"Learning rate: \", η)\n",
        "\n",
        "      print(\"Epoch \", end=\"\")\n",
        "      for j in range(epochs):\n",
        "        print(\".\", end=\"\")\n",
        "        y_0 = f(z_list(x))\n",
        "\n",
        "        # updating hidden layer\n",
        "        for i in range(hidden_neurons):\n",
        "          hidden_layer_weights[i] = hidden_layer_weights[i] - η * deriv_hidden_weight(y_0, i)\n",
        "\n",
        "        # updating outer layer\n",
        "        for i in range(hidden_neurons):\n",
        "          out_weights[i] = out_weights[i] - η * deriv_outer_weight(y_0, i)\n",
        "\n",
        "      print()\n",
        "\n",
        "\n",
        "    print(\"\\nTraining complete!\")\n",
        "\n",
        "    print(\"\\nEnding values:\")\n",
        "    print(\"Hidden Layer Weights: \")\n",
        "    for i in range(hidden_neurons):\n",
        "      print(f\"Weight w{i}     : \", hidden_layer_weights[i])\n",
        "\n",
        "    print(\"Outer Layer Weights: \")\n",
        "    for i in range(hidden_neurons):\n",
        "      print(f\"Weight w{i}     : \", hidden_layer_weights[i])\n",
        "\n",
        "    y0_arr2 = np.zeros(len(x_arr))\n",
        "    for i in range(len(x_arr)):\n",
        "      x = x_arr[i]\n",
        "      y0_arr2[i] = f(z_list(x))\n",
        "      print(\"Input (x)    : \", x, end = \" --> \")\n",
        "      print(\"Output (y_0) : \", f(z_list(x)))\n",
        "\n",
        "\n",
        "    print()\n",
        "\n",
        "    print(\"This NN is NOT trained on the following inputs.\")\n",
        "    print(\"Input (x)    : \", 6, end = \" --> \")\n",
        "    print(\"Output (y_0) : \", f(z_list(6)))\n",
        "    print(\"Input (x)    : \", 7, end = \" --> \")\n",
        "    print(\"Output (y_0) : \", f(z_list(7)))\n",
        "    print(\"Input (x)    : \", 8, end = \" --> \")\n",
        "    print(\"Output (y_0) : \", f(z_list(8)))\n",
        "    print(\"Input (x)    : \", 9, end = \" --> \")\n",
        "    print(\"Output (y_0) : \", f(z_list(9)))\n",
        "    print(\"Input (x)    : \", 10, end = \" --> \")\n",
        "    print(\"Output (y_0) : \", f(z_list(10)))\n",
        "\n",
        "lecture_learning_training(15)"
      ],
      "metadata": {
        "id": "_rAPi4D1Mwu9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# graveyard\n",
        "# # EXAMPLE USAGE\n",
        "# if (__name__ == \"__main__\"):\n",
        "#     # Initializing Student\n",
        "#     act_func = Γ                                         # pass activation function\n",
        "#     example_student = Student(1, act_func, 5)\n",
        "\n",
        "#     # Changing Activation Function\n",
        "#     new_act_func = relu\n",
        "#     example_student.set_activation_function(new_act_func)\n",
        "\n",
        "#     # Changing # of Hidden Nodes\n",
        "#     example_student.set_hidden_neurons(10)\n",
        "\n",
        "#     # Accessing Activation Function\n",
        "#     print(example_student.get_activation_function().__name__)\n",
        "\n",
        "#     # Accessing # of Hidden Nodes\n",
        "#     print(example_student.get_hidden_neurons())"
      ],
      "metadata": {
        "id": "gcN91zkCKjKd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}