23. MiniVGGNet from Scratch on CIFAR-10 with BN.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":"# MiniVGGNet from Scratch using Keras on CIFAR 10. ","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"# [](http://)","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"![](https://www.researchgate.net/publication/332435757/figure/fig1/AS:748140944703488@1555382109474/Architecture-of-MiniVGGNet.jpg)","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"### Importing required libraries","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.models import Sequential\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.convolutional import MaxPooling2D\nfrom keras.layers.core import Activation\nfrom keras.layers.core import Flatten\nfrom keras.layers.core import Dropout\nfrom keras.layers.core import Dense\nfrom keras import backend as K\nfrom sklearn.preprocessing import LabelBinarizer\nfrom sklearn.metrics import classification_report\nfrom keras.optimizers import SGD\nfrom keras.datasets import cifar10\nimport matplotlib.pyplot as plt\nimport numpy as np\n%matplotlib inline","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### A function MiniVGGNet() which takes input of (width, height, depth and number of classes) and model is defined. ","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"def MiniVGGNet(width, height, depth, classes):\n    model = Sequential()\n    inputShape = (height, width, depth)\n    chanDim = -1\n\n    if K.image_data_format()==\"channels_first\":\n        inputShape = (depth, height, width)\n        chanDim = 1\n\n    model.add(Conv2D(32, (3,3), padding = \"same\", input_shape = inputShape))\n    model.add(Activation(\"relu\"))\n    model.add(BatchNormalization(axis=chanDim))\n    model.add(Conv2D(32, (3,3), padding = \"same\"))\n    model.add(Activation(\"relu\"))\n    model.add(BatchNormalization(axis=chanDim))\n    model.add(MaxPooling2D(pool_size=(2,2)))\n    model.add(Dropout(0.25))\n    model.add(Conv2D(64, (3,3), padding = \"same\"))\n    model.add(Activation(\"relu\"))\n    model.add(BatchNormalization(axis=chanDim))\n    model.add(Conv2D(64, (3,3), padding = \"same\"))\n    model.add(Activation(\"relu\"))\n    model.add(BatchNormalization(axis=chanDim))\n    model.add(MaxPooling2D(pool_size=(2,2)))\n    model.add(Dropout(0.25))\n    model.add(Flatten())\n    model.add(Dense(512))\n    model.add(Activation(\"relu\"))\n    model.add(BatchNormalization())\n    model.add(Dropout(0.5))\n    model.add(Dense(classes))\n    model.add(Activation(\"softmax\"))\n    return model","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Loading CIFAR-10 Dataset","execution_count":null},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"print(\"Loading CIFAR-10 data . . . \")\n((trainX, trainY), (testX, testY)) = cifar10.load_data()\n\ntrainX = trainX.astype(\"float\") / 255.0\ntestX = testX.astype(\"float\") / 255.0","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Convert the labels from integers to vectors","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"lb = LabelBinarizer()\ntrainY = lb.fit_transform(trainY)\ntestY = lb.transform(testY)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Initialize the label names for the CIFAR-10 dataset","execution_count":null},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"labelNames = [\"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\", \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Compile the model with loss, optimizer and metric","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Compiling Model . . .\")\nopt = SGD(lr=0.01, decay=0.01 / 40, momentum=0.9, nesterov=True)\nmodel = MiniVGGNet(width=32, height=32, depth=3, classes=10)\nmodel.compile(loss=\"categorical_crossentropy\", optimizer=opt, metrics=[\"accuracy\"])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Train the network","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Training Neural Network . . .\")\nH = model.fit(trainX, trainY, validation_data=(testX, testY),\nbatch_size=64, epochs=40, verbose=1)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Evaluate the network","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Evaluating Neural network...\")\npredictions = model.predict(testX, batch_size=64)\nprint(classification_report(testY.argmax(axis=1), predictions.argmax(axis=1), target_names=labelNames))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"acc = H.history['accuracy']\nval_acc = H.history['val_accuracy']\nloss = H.history['loss']\nval_loss = H.history['val_loss']\n\nepochs = range(1, len(acc) + 1)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Plot of Training Accuracy Vs Validation Accuracy","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(epochs, acc, 'bo', label='Training acc')\nplt.plot(epochs, val_acc, 'b', label='Validation acc')\nplt.title('Training and Validation accuracy')\nplt.legend()\nplt.figure()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Plot of Training Loss Vs Validation Loss","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(epochs, loss, 'bo', label='Training loss')\nplt.plot(epochs, val_loss, 'b', label='Validation loss')\nplt.title('Training and Validation loss')\nplt.legend()\nplt.figure()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### End of Notebook, if you like it then upvote it. ","execution_count":null}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}