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Augmentor_Keras.ipynb
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Augmentor_Keras.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Training a Neural Network using Augmentor and Keras\n",
"\n",
"In this notebook, we will train a simple convolutional neural network on the MNIST dataset using Augmentor to augment images on the fly using a generator.\n",
"\n",
"## Import Required Libraries\n",
"\n",
"We start by making a number of imports:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import Augmentor\n",
"\n",
"import keras\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Flatten\n",
"from keras.layers import Conv2D, MaxPooling2D\n",
"\n",
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define a Convolutional Neural Network\n",
"\n",
"Once the libraries have been imported, we define a small convolutional neural network. See the Keras documentation for details of this network: <https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py> \n",
"\n",
"It is a three layer deep neural network, consisting of 2 convolutional layers and a fully connected layer:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"num_classes = 10\n",
"input_shape = (28, 28, 1)\n",
"\n",
"model = Sequential()\n",
"model.add(Conv2D(32, kernel_size=(3, 3),\n",
" activation='relu',\n",
" input_shape=input_shape))\n",
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"model.add(Dropout(0.25))\n",
"model.add(Flatten())\n",
"model.add(Dense(128, activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(num_classes, activation='softmax'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once a network has been defined, you can compile it so that the model is ready to be trained with data:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"model.compile(loss=keras.losses.categorical_crossentropy,\n",
" optimizer=keras.optimizers.Adadelta(),\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can view a summary of the network using the `summary()` function:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d_1 (Conv2D) (None, 26, 26, 32) 320 \n",
"_________________________________________________________________\n",
"conv2d_2 (Conv2D) (None, 24, 24, 64) 18496 \n",
"_________________________________________________________________\n",
"max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64) 0 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 12, 12, 64) 0 \n",
"_________________________________________________________________\n",
"flatten_1 (Flatten) (None, 9216) 0 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 128) 1179776 \n",
"_________________________________________________________________\n",
"dropout_2 (Dropout) (None, 128) 0 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 10) 1290 \n",
"=================================================================\n",
"Total params: 1,199,882\n",
"Trainable params: 1,199,882\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use Augmentor to Scan Directory for Data\n",
"\n",
"Now we will use Augmentor to scan a directory containing our data that we will eventually feed into the neural network in order to train it. \n",
"\n",
"When you point a pipeline to a directory, it will scan each subdirectory and treat each subdirectory as a class for your machine learning problem. \n",
"\n",
"For example, within the directory `mnist`, there are subdirectories for each digit:\n",
"\n",
"```\n",
"mnist/\n",
"├── 0/\n",
"│ ├── 0001.png\n",
"│ ├── 0002.png\n",
"│ ├── ...\n",
"│ └── 5985.png\n",
"├── 1/\n",
"│ ├── 0001.png\n",
"│ ├── 0002.png\n",
"│ ├── ...\n",
"│ └── 6101.png\n",
"├── 2/\n",
"│ ├── 0000.png\n",
"│ ├── 0001.png\n",
"│ ├── ...\n",
"│ └── 5801.png\n",
"│ ...\n",
"├── 9/\n",
"│ ├── 0001.png\n",
"│ ├── 0002.png\n",
"│ ├── ...\n",
"│ └── 6001.png\n",
"└\n",
"```\n",
"\n",
"The directory `0` contains all the images corresponding to the 0 class.\n",
"\n",
"To get the data, we can use `wget` (this may not work under Windows):"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2018-03-23 15:15:37-- https://rawgit.com/myleott/mnist_png/master/mnist_png.tar.gz\n",
"Resolving rawgit.com (rawgit.com)... 104.18.62.176, 104.18.63.176, 2400:cb00:2048:1::6812:3eb0, ...\n",
"Connecting to rawgit.com (rawgit.com)|104.18.62.176|:443... connected.\n",
"HTTP request sent, awaiting response... 301 Moved Permanently\n",
"Location: https://raw.githubusercontent.com/myleott/mnist_png/master/mnist_png.tar.gz [following]\n",
"--2018-03-23 15:15:37-- https://raw.githubusercontent.com/myleott/mnist_png/master/mnist_png.tar.gz\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.112.133\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.112.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 15683414 (15M) [application/octet-stream]\n",
"Saving to: ‘mnist_png.tar.gz’\n",
"\n",
"100%[======================================>] 15,683,414 9.06MB/s in 1.7s \n",
"\n",
"2018-03-23 15:15:38 (9.06 MB/s) - ‘mnist_png.tar.gz’ saved [15683414/15683414]\n",
"\n"
]
}
],
"source": [
"!wget https://rawgit.com/myleott/mnist_png/master/mnist_png.tar.gz\n",
"!tar -xf mnist_png.tar.gz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After the MNIST data has downloaded, we can instantiate a `Pipeline` object in the `training` directory to add the images to the current pipeline:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initialised with 60000 image(s) found.\n",
"Output directory set to mnist_png/training/output."
]
}
],
"source": [
"p = Augmentor.Pipeline(\"mnist_png/training\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Add Operations to the Pipeline\n",
"\n",
"Now that a pipeline object `p` has been created, we can add operations to the pipeline. Below we add several simple operations:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"p.flip_top_bottom(probability=0.1)\n",
"p.rotate(probability=0.3, max_left_rotation=5, max_right_rotation=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can view the status of pipeline using the `status()` function, which shows information regarding the number of classes in the pipeline, the number of images, and what operations have been added to the pipeline:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Operations: 2\n",
"\t0: Flip (top_bottom_left_right=TOP_BOTTOM probability=0.1 )\n",
"\t1: RotateRange (max_right_rotation=5.0 max_left_rotation=-5.0 probability=0.3 )\n",
"Images: 60000\n",
"Classes: 10\n",
"\tClass index: 0 Class label: 0 \n",
"\tClass index: 1 Class label: 1 \n",
"\tClass index: 2 Class label: 2 \n",
"\tClass index: 3 Class label: 3 \n",
"\tClass index: 4 Class label: 4 \n",
"\tClass index: 5 Class label: 5 \n",
"\tClass index: 6 Class label: 6 \n",
"\tClass index: 7 Class label: 7 \n",
"\tClass index: 8 Class label: 8 \n",
"\tClass index: 9 Class label: 9 \n",
"Dimensions: 1\n",
"\tWidth: 28 Height: 28\n",
"Formats: 1\n",
"\t PNG\n",
"\n",
"You can remove operations using the appropriate index and the remove_operation(index) function.\n"
]
}
],
"source": [
"p.status()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating a Generator\n",
"\n",
"A generator will create images indefinitely, and we can use this generator as input into the model created above. The generator is created with a user-defined batch size, which we define here in a variable named `batch_size`. This is used later to define number of steps per epoch, so it is best to keep it stored as a variable."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"batch_size = 128\n",
"g = p.keras_generator(batch_size=batch_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The generator can now be used to created augmented data. In Python, generators are invoked using the `next()` function - the Augmentor generators will return images indefinitely, and so `next()` can be called as often as required. \n",
"\n",
"You can view the output of generator manually:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"images, labels = next(g)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Images, and their labels, are returned in batches of the size defined above by `batch_size`. The `image_batch` variable is a tuple, containing the augmentented images and their corresponding labels.\n",
"\n",
"To see the label of the first image returned by the generator you can use the array's index:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 0 0 1 0 0 0 0]\n"
]
}
],
"source": [
"print(labels[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or preview the images using Matplotlib (the image should be a 5, according to the label information above):"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7ff6e6ffa550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(images[0].reshape(28, 28), cmap=\"Greys\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Network\n",
"\n",
"We train the network by passing the generator, `g`, to the model's fit function. In Keras, if a generator is used we used the `fit_generator()` function as opposed to the standard `fit()` function. Also, the steps per epoch should roughly equal the total number of images in your dataset divided by the `batch_size`.\n",
"\n",
"Training the network over 5 epochs, we get the following output:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"468/468 [==============================] - 30s 65ms/step - loss: 0.4860 - acc: 0.8478\n",
"Epoch 2/5\n",
"468/468 [==============================] - 29s 63ms/step - loss: 0.2026 - acc: 0.9392\n",
"Epoch 3/5\n",
"468/468 [==============================] - 29s 61ms/step - loss: 0.1611 - acc: 0.9523\n",
"Epoch 4/5\n",
"468/468 [==============================] - 28s 60ms/step - loss: 0.1405 - acc: 0.9582\n",
"Epoch 5/5\n",
"468/468 [==============================] - 28s 59ms/step - loss: 0.1203 - acc: 0.9645\n"
]
}
],
"source": [
"h = model.fit_generator(g, steps_per_epoch=len(p.augmentor_images)/batch_size, epochs=5, verbose=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary\n",
"\n",
"Using Augmentor with Keras means only that you need to create a generator when you are finished creating your pipeline. This has the advantage that no images need to be saved to disk and are augmented on the fly."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}