From f9fdcbe8549665c02151fd413587c9887355c79b Mon Sep 17 00:00:00 2001 From: Rohit Midha Date: Sun, 13 Jan 2019 18:31:53 +0530 Subject: [PATCH] Added: Images --- Neural Networks Decision Boundary.ipynb | 286 ------------------ .../Neural Networks Decision Boundary.md | 159 ---------- .../output_3_1.png => dataset_outlook.png | Bin decision-boundary.png | Bin 0 -> 112893 bytes 4 files changed, 445 deletions(-) delete mode 100644 Neural Networks Decision Boundary.ipynb delete mode 100644 Neural Networks Decision Boundary/Neural Networks Decision Boundary.md rename Neural Networks Decision Boundary/output_3_1.png => dataset_outlook.png (100%) create mode 100644 decision-boundary.png diff --git a/Neural Networks Decision Boundary.ipynb b/Neural Networks Decision Boundary.ipynb deleted file mode 100644 index 9642bf9..0000000 --- a/Neural Networks Decision Boundary.ipynb +++ /dev/null @@ -1,286 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Start off by importing all the required packages" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "np.random.seed(0)\n", - "from sklearn import datasets\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense\n", - "from keras.optimizers import SGD" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we generate the dataset and the scatter plot." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1000,), (1000, 2))" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "image/png": { - "height": 250, - "width": 388 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "X, y = datasets.make_moons(n_samples=1000, noise=0.1, random_state=0)\n", - "colors = ['blue' if label == 1 else 'red' for label in y]\n", - "plt.scatter(X[:,0], X[:,1], color=colors)\n", - "y.shape, X.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Defining the Model \n", - "The Keras Python library makes creating deep learning models fast and easy.\n", - "\n", - "The sequential API allows you to create models layer-by-layer for most problems. Keras has different activation functions built in such as 'sigmoid', 'tanh', 'softmax', and [many others](https://keras.io/optimizers/). Also built in are different weight initialization options. We use the sigmoid activation which limits the values to $[-\\epsilon,\\epsilon]$\n", - "\n", - "This initialization method corresponds to the `'glorot_uniform'` initialization option in Keras." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:8: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_shape=(2,), activation=\"sigmoid\", units=5, kernel_initializer=\"glorot_uniform\")`\n", - " \n", - "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"sigmoid\", units=5, kernel_initializer=\"glorot_uniform\")`\n", - " if __name__ == '__main__':\n", - "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:10: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"sigmoid\", units=1, kernel_initializer=\"glorot_uniform\")`\n", - " # Remove the CWD from sys.path while we load stuff.\n" - ] - } - ], - "source": [ - "# Define our model object\n", - "model = Sequential()\n", - "\n", - "# kwarg dict for convenience\n", - "layer_kw = dict(activation='sigmoid', init='glorot_uniform')\n", - "\n", - "# Add layers to our model\n", - "model.add(Dense(output_dim=5, input_shape=(2, ), **layer_kw))\n", - "model.add(Dense(output_dim=5, **layer_kw))\n", - "model.add(Dense(output_dim=1, **layer_kw))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Defining the Optimizer \n", - "While initializing the Keras model we also need to specify an optimizer such as SGD or Adam or RMSProp. In this case we will use SGD. You can however read more about them [here](https://keras.io/optimizers/)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "sgd = SGD(lr=0.001)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compile the Model \n", - "A loss function (or objective function, or optimization score function) is one of the two parameters required to compile a model, the other being the optimizer itself. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "model.compile(optimizer=sgd, loss='binary_crossentropy')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summarize the Model\n", - "Keras allows for models to be summarized using `model.summary()`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense_4 (Dense) (None, 5) 15 \n", - "_________________________________________________________________\n", - "dense_5 (Dense) (None, 5) 30 \n", - "_________________________________________________________________\n", - "dense_6 (Dense) (None, 1) 6 \n", - "=================================================================\n", - "Total params: 51\n", - "Trainable params: 51\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train the Model\n", - "We can now train our model using the `model.fit()` method. We will save this as history to refer to it at a future point. Since the number of epochs is very high we will set `verbose=0` and shuffle the data as well. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n", - " \"\"\"Entry point for launching an IPython kernel.\n" - ] - } - ], - "source": [ - "history = model.fit(X[:500], y[:500], verbose=0, nb_epoch=4000, shuffle=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualizing the Results" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_decision_boundary(X, y, model, steps=1000, cmap='Paired'):\n", - " \"\"\"\n", - " Function to plot the decision boundary and data points of a model.\n", - " Data points are colored based on their actual label.\n", - " \"\"\"\n", - " cmap = plt.get_cmap(cmap)\n", - " \n", - " # Define region of interest by data limits\n", - " xmin, xmax = X[:,0].min() - 1, X[:,0].max() + 1\n", - " ymin, ymax = X[:,1].min() - 1, X[:,1].max() + 1\n", - " steps = 1000\n", - " x_span = np.linspace(xmin, xmax, steps)\n", - " y_span = np.linspace(ymin, ymax, steps)\n", - " xx, yy = np.meshgrid(x_span, y_span)\n", - "\n", - " # Make predictions across region of interest\n", - " labels = model.predict(np.c_[xx.ravel(), yy.ravel()])\n", - "\n", - " # Plot decision boundary in region of interest\n", - " z = labels.reshape(xx.shape)\n", - " \n", - " fig, ax = plt.subplots()\n", - " ax.contourf(xx, yy, z, cmap=cmap, alpha=0.5)\n", - "\n", - " # Get predicted labels on training data and plot\n", - " train_labels = model.predict(X)\n", - " ax.scatter(X[:,0], X[:,1], c=y, cmap=cmap, lw=0)\n", - " \n", - " return fig, ax" - ] - }, - { - "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": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Neural Networks Decision Boundary/Neural Networks Decision Boundary.md b/Neural Networks Decision Boundary/Neural Networks Decision Boundary.md deleted file mode 100644 index 5e1e5c1..0000000 --- a/Neural Networks Decision Boundary/Neural Networks Decision Boundary.md +++ /dev/null @@ -1,159 +0,0 @@ - -Start off by importing all the required packages - - -```python -import numpy as np -np.random.seed(0) -from sklearn import datasets -import matplotlib.pyplot as plt -%matplotlib inline - -from keras.models import Sequential -from keras.layers import Dense -from keras.optimizers import SGD -``` - -Next we generate the dataset and the scatter plot. - - -```python -X, y = datasets.make_moons(n_samples=1000, noise=0.1, random_state=0) -colors = ['blue' if label == 1 else 'red' for label in y] -plt.scatter(X[:,0], X[:,1], color=colors) -y.shape, X.shape -``` - - - - - ((1000,), (1000, 2)) - - - - -![png](output_3_1.png) - - -## Defining the Model -The Keras Python library makes creating deep learning models fast and easy. - -The sequential API allows you to create models layer-by-layer for most problems. Keras has different activation functions built in such as 'sigmoid', 'tanh', 'softmax', and [many others](https://keras.io/optimizers/). Also built in are different weight initialization options. We use the sigmoid activation which limits the values to $[-\epsilon,\epsilon]$ - -This initialization method corresponds to the `'glorot_uniform'` initialization option in Keras. - - -```python -# Define our model object -model = Sequential() - -# kwarg dict for convenience -layer_kw = dict(activation='sigmoid', init='glorot_uniform') - -# Add layers to our model -model.add(Dense(output_dim=5, input_shape=(2, ), **layer_kw)) -model.add(Dense(output_dim=5, **layer_kw)) -model.add(Dense(output_dim=1, **layer_kw)) -``` - - /anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:8: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_shape=(2,), activation="sigmoid", units=5, kernel_initializer="glorot_uniform")` - - /anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation="sigmoid", units=5, kernel_initializer="glorot_uniform")` - if __name__ == '__main__': - /anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:10: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation="sigmoid", units=1, kernel_initializer="glorot_uniform")` - # Remove the CWD from sys.path while we load stuff. - - -## Defining the Optimizer -While initializing the Keras model we also need to specify an optimizer such as SGD or Adam or RMSProp. In this case we will use SGD. You can however read more about them [here](https://keras.io/optimizers/). - - -```python -sgd = SGD(lr=0.001) -``` - -## Compile the Model -A loss function (or objective function, or optimization score function) is one of the two parameters required to compile a model, the other being the optimizer itself. - - - -```python -model.compile(optimizer=sgd, loss='binary_crossentropy') -``` - -## Summarize the Model -Keras allows for models to be summarized using `model.summary()`. - - - -```python -model.summary() -``` - - _________________________________________________________________ - Layer (type) Output Shape Param # - ================================================================= - dense_4 (Dense) (None, 5) 15 - _________________________________________________________________ - dense_5 (Dense) (None, 5) 30 - _________________________________________________________________ - dense_6 (Dense) (None, 1) 6 - ================================================================= - Total params: 51 - Trainable params: 51 - Non-trainable params: 0 - _________________________________________________________________ - - -## Train the Model -We can now train our model using the `model.fit()` method. We will save this as history to refer to it at a future point. Since the number of epochs is very high we will set `verbose=0` and shuffle the data as well. - - - -```python -history = model.fit(X[:500], y[:500], verbose=0, nb_epoch=4000, shuffle=True) -``` - - /anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`. - """Entry point for launching an IPython kernel. - - -## Visualizing the Results - - -```python -def plot_decision_boundary(X, y, model, steps=1000, cmap='Paired'): - """ - Function to plot the decision boundary and data points of a model. - Data points are colored based on their actual label. - """ - cmap = plt.get_cmap(cmap) - - # Define region of interest by data limits - xmin, xmax = X[:,0].min() - 1, X[:,0].max() + 1 - ymin, ymax = X[:,1].min() - 1, X[:,1].max() + 1 - steps = 1000 - x_span = np.linspace(xmin, xmax, steps) - y_span = np.linspace(ymin, ymax, steps) - xx, yy = np.meshgrid(x_span, y_span) - - # Make predictions across region of interest - labels = model.predict(np.c_[xx.ravel(), yy.ravel()]) - - # Plot decision boundary in region of interest - z = labels.reshape(xx.shape) - - fig, ax = plt.subplots() - ax.contourf(xx, yy, z, cmap=cmap, alpha=0.5) - - # Get predicted labels on training data and plot - train_labels = model.predict(X) - ax.scatter(X[:,0], X[:,1], c=y, cmap=cmap, lw=0) - - return fig, ax -``` - - -```python - -``` diff --git a/Neural Networks Decision Boundary/output_3_1.png b/dataset_outlook.png similarity index 100% rename from Neural Networks Decision Boundary/output_3_1.png rename to dataset_outlook.png diff --git a/decision-boundary.png b/decision-boundary.png new file mode 100644 index 0000000000000000000000000000000000000000..46248e0228161db75463c993ee81b15e38394607 GIT binary patch literal 112893 zcmZU)18^nX^FJKhwr$%sZfx6jHp#}>*fw`#+vdi$xv`TskG{Wu)qA^c)y$cL=`)Q_ zch5v9DM-S@;J^R@0l`a4i2;CsfET|0x=;{bEjbWmM?gSWT2`W>O46dDL`u#M=2o_5 zKtP%y$zI^9YKoXwS!<7d#qIHz_9vGL%B`;7QzeAK#6-l-}LgpuTg#DzrA;!%KW 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