From a8b67a2feb4bd22647592ea948949169dc7b9763 Mon Sep 17 00:00:00 2001
From: Rokas Puzonas <rokas.puz@gmail.com>
Date: Thu, 7 Mar 2024 02:24:14 +0200
Subject: [PATCH] remove temporary file

---
 lab2-tmp.py | 101 ----------------------------------------------------
 1 file changed, 101 deletions(-)
 delete mode 100644 lab2-tmp.py

diff --git a/lab2-tmp.py b/lab2-tmp.py
deleted file mode 100644
index 7496610..0000000
--- a/lab2-tmp.py
+++ /dev/null
@@ -1,101 +0,0 @@
-from dataclasses import dataclass
-import numpy as np
-from typing import Literal
-
-@dataclass
-class NeuralNetworkLayer:
-    weights: np.array
-    bias: np.array
-    activation: Literal["relu", "sigmoid"]
-
-@dataclass
-class NeuralNetwork:
-    layers: list[NeuralNetworkLayer]
-
-def init_network(architecture, seed=1):
-    np.random.seed(seed)
-
-    layers = []
-    for i in range(len(architecture) - 1):
-        layer_input_size = architecture[i]["size"]
-        layer_output_size = architecture[i+1]["size"]
-
-        layers.append(NeuralNetworkLayer(
-            np.random.randn(layer_output_size, layer_input_size) * 0.01,
-            np.random.randn(layer_output_size, 1) * 0.1,
-            architecture[i+1]["activation"]
-        ))
-
-    return NeuralNetwork(layers)
-
-def relu(Z):
-    return np.maximum(0, Z)
-
-def sigmoid(Z):
-    return 1/(1+np.exp(-Z))
-
-def single_layer_forward_propagation(A_prev, W_curr, b_curr, activation="relu"):
-    Z_curr = np.dot(W_curr, A_prev) + b_curr
-
-    if activation == "relu":
-        activation_func = relu
-    elif activation == "sigmoid":
-        activation_func = sigmoid
-    else:
-        raise Exception(f"Non-supported activation function: '{activation}'")
-
-    return activation_func(Z_curr), Z_curr
-
-def get_cost_value(Y_hat, Y):
-    # number of examples
-    m = Y_hat.shape[1]
-    # calculation of the cost according to the formula
-    cost = -1 / m * (np.dot(Y, np.log(Y_hat).T) + np.dot(1 - Y, np.log(1 - Y_hat).T))
-    return np.squeeze(cost)
-
-def full_forward_propagation(X, network: NeuralNetwork):
-    A_values = []
-    Z_values = []
-
-    A_curr = X
-    for layer in network.layers:
-        A_prev = A_curr
-
-        W_curr = layer.weights
-        b_curr = layer.bias
-        A_curr, Z_curr = single_layer_forward_propagation(A_prev, W_curr, b_curr, layer.activation)
-
-        A_values.append(A_curr)
-        Z_values.append(Z_curr)
-
-    return A_curr, A_values, Z_values
-
-def train(X, Y, network: NeuralNetwork, epochs, learning_rate, verbose=False, callback=None):
-    print(X)
-    print(Y)
-
-    cost_history = []
-    accuracy_history = []
-
-    for i in range(epochs):
-        Y_hat, A_values, Z_values = full_forward_propagation(X, network)
-
-    return cost_history, accuracy_history
-
-def main(architecture):
-    network = init_network(architecture)
-
-    X_train = np.array([[0,0],[0,1],[1,0],[1,1]])
-    Y_train = np.array([0,1,1,1])
-    cost_history, accuracy_history = train(X_train.T, np.transpose(Y_train.reshape((Y_train.shape[0], 1))), network, 1000, 0.1)
-
-
-
-main(architecture = [
-    {"size":  2},
-    {"size": 25, "activation": "relu"},
-    {"size": 50, "activation": "relu"},
-    {"size": 50, "activation": "relu"},
-    {"size": 25, "activation": "relu"},
-    {"size":  1, "activation": "sigmoid"}
-])