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@amrithm98
amrithm98 committed Sep 17, 2017
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3 changes: 3 additions & 0 deletions .gitignore
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dataset/
lekha-OCR-database/
mal_dataset.tar
190 changes: 190 additions & 0 deletions .ipynb_checkpoints/Malayalam-Character-Recognition-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from keras.preprocessing import image\n",
"from keras.applications import imagenet_utils\n",
"import numpy as np\n",
"from keras import applications\n",
"from keras.preprocessing.image import ImageDataGenerator\n",
"from keras import optimizers\n",
"from keras.models import Sequential,Model\n",
"from keras.layers.normalization import BatchNormalization\n",
"from keras.models import model_from_json\n",
"from keras.layers import Dense, Activation, Dropout, Flatten\n",
"from keras.layers import Convolution2D\n",
"from keras.layers import MaxPooling2D\n",
"from keras.layers import ZeroPadding2D"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 34553 images belonging to 133 classes.\n"
]
}
],
"source": [
"train_datagen = image.ImageDataGenerator(\n",
" rescale=1./255,\n",
" shear_range=0.2,\n",
" zoom_range=0.2,\n",
" horizontal_flip=True)\n",
"\n",
"test_datagen = image.ImageDataGenerator(rescale=1./255)\n",
"\n",
"train_generator = train_datagen.flow_from_directory(\n",
" '/home/amrith/Machine-Learning/MalayalamOCR/dataset',\n",
" target_size=(32, 32),\n",
" batch_size=16,\n",
" class_mode='binary')\n",
"\n",
"# validation_generator = test_datagen.flow_from_directory(\n",
"# '/home/amrith/Machine-Learning/FastAI/courses/dogscats/sample/valid/',\n",
"# target_size=(64, 64),\n",
"# batch_size=16,\n",
"# class_mode='binary')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def create_model():\n",
" model=Sequential()\n",
" model.add(Convolution2D(64,(3,3),input_shape=(32,32,3),activation='relu',padding='same'))\n",
" model.add(Convolution2D(64,(3,3),activation='relu',padding='same'))\n",
" model.add(MaxPooling2D((2, 2), strides=(2, 2)))\n",
" model.add(Convolution2D(128,(3,3),activation='relu',padding='same'))\n",
" model.add(Convolution2D(128,(3,3),activation='relu',padding='same'))\n",
" model.add(MaxPooling2D((2, 2), strides=(2, 2)))\n",
" model.add(Flatten())\n",
" model.add(Dense(4096, activation='relu'))\n",
" model.add(Dropout(0.5))\n",
" model.add(Dense(4096, activation='relu'))\n",
" model.add(Dropout(0.5))\n",
" model.add(Dense(133, activation='softmax'))\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"model=create_model()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d_17 (Conv2D) (None, 32, 32, 64) 1792 \n",
"_________________________________________________________________\n",
"conv2d_18 (Conv2D) (None, 32, 32, 64) 36928 \n",
"_________________________________________________________________\n",
"max_pooling2d_9 (MaxPooling2 (None, 16, 16, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_19 (Conv2D) (None, 16, 16, 128) 73856 \n",
"_________________________________________________________________\n",
"conv2d_20 (Conv2D) (None, 16, 16, 128) 147584 \n",
"_________________________________________________________________\n",
"max_pooling2d_10 (MaxPooling (None, 8, 8, 128) 0 \n",
"_________________________________________________________________\n",
"flatten_5 (Flatten) (None, 8192) 0 \n",
"_________________________________________________________________\n",
"dense_13 (Dense) (None, 4096) 33558528 \n",
"_________________________________________________________________\n",
"dropout_9 (Dropout) (None, 4096) 0 \n",
"_________________________________________________________________\n",
"dense_14 (Dense) (None, 4096) 16781312 \n",
"_________________________________________________________________\n",
"dropout_10 (Dropout) (None, 4096) 0 \n",
"_________________________________________________________________\n",
"dense_15 (Dense) (None, 133) 544901 \n",
"=================================================================\n",
"Total params: 51,144,901\n",
"Trainable params: 51,144,901\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model.compile(optimizer='adagrad',\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING (theano.tensor.blas): We did not found a dynamic library into the library_dir of the library we use for blas. If you use ATLAS, make sure to compile it with dynamics library.\n"
]
}
],
"source": [
"model.fit_generator(train_generator,steps_per_epoch=2000,epochs=50)"
]
}
],
"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": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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