CVML-MachineLearning/course/1 First Deep Learning Model.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First Deep Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_circles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_circles(n_samples=1000,\n",
    "                    noise=0.1,\n",
    "                    factor=0.2,\n",
    "                    random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
    "plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
    "plt.xlim(-1.5, 1.5)\n",
    "plt.ylim(-1.5, 1.5)\n",
    "plt.legend(['0', '1'])\n",
    "plt.title(\"Blue circles and Red crosses\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(4, input_shape=(2,), activation='tanh'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(SGD(learning_rate=0.5), 'binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X, y, epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hticks = np.linspace(-1.5, 1.5, 101)\n",
    "vticks = np.linspace(-1.5, 1.5, 101)\n",
    "aa, bb = np.meshgrid(hticks, vticks)\n",
    "ab = np.c_[aa.ravel(), bb.ravel()]\n",
    "c = model.predict(ab)\n",
    "cc = c.reshape(aa.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "plt.contourf(aa, bb, cc, cmap='bwr', alpha=0.2)\n",
    "plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
    "plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
    "plt.xlim(-1.5, 1.5)\n",
    "plt.ylim(-1.5, 1.5)\n",
    "plt.legend(['0', '1'])\n",
    "plt.title(\"Blue circles and Red crosses\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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