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course/1 First Deep Learning Model-Copy1.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 # import the numpy library and assign the name np to it\n",
+    "%matplotlib inline # magic function that sets the backend of matplotlib to the inline backend\n",
+    "import matplotlib.pyplot as plt # import the matplotlib.pyplot and assign the name plt to it"
+   ]
+  },
+  {
+   "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": []
+  }
+ ],
+ "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": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}