funciones-de-activacion-y-perdida.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.7"},"colab":{"name":"Funciones de activacion y perdida.ipynb","provenance":[]}},"cells":[{"cell_type":"code","metadata":{"id":"K6_wqe02jLye"},"source":["import numpy as np\n","import matplotlib.pyplot as plt"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xpmDFLHtj08m"},"source":["## Funciones de activación"]},{"cell_type":"code","metadata":{"id":"HxjRMw2ijLyp"},"source":["def sigmoid(a, derivate = False):\n","    if derivate:\n","        return np.exp(-a)/(( np.exp(-a) +1)**2)\n","    else:    \n","        return 1 / (1 + np.exp(-a))\n","\n","def steep(x, derivate = False):    \n","    return np.piecewise(x,[x<0.0,x>=0.0],[0,1])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Etj97bCCjLyq"},"source":["x = np.linspace(10,-10,100)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rG89lw9rjLyu","outputId":"5dfff496-fb4a-4b5c-d3da-9ed16e38acdc"},"source":["plt.plot(x,sigmoid(x))"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"heG4tatkjLy0","outputId":"e3aa206d-ce47-4427-c9d8-ea6df5fd3877"},"source":["plt.plot(x,steep(x))"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7f3a05ceb450>]"]},"metadata":{"tags":[]},"execution_count":9},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"5NmxYUWykDLM"},"source":["## Función de pérdida o coste"]},{"cell_type":"code","metadata":{"id":"zGms-U8njLy1"},"source":["def mse(y,y_hat,derivate=False):\n","    if derivate:\n","        return (y_hat - y)\n","    else:            \n","        return np.mean((y_hat - y)**2)\n","    "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jw47TySVjLy2"},"source":["prediction = np.array([0.9,0.5,0.2,0.0])\n","real =  np.array([0,0,1,1])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"RSa-ImU9jLy2","outputId":"4a852a3c-c2f7-4b03-9b76-42f89ba108c5"},"source":["mse(real, prediction)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.675"]},"metadata":{"tags":[]},"execution_count":20}]}]}