{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Flatten\n",
"from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
"from keras.utils import np_utils\n",
"from keras.utils.vis_utils import model_to_dot\n",
"from IPython.display import SVG\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From D:\\Programing\\Anaconda3\\envs\\tf1_env\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
"\n",
"WARNING:tensorflow:From D:\\Programing\\Anaconda3\\envs\\tf1_env\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
"\n",
"WARNING:tensorflow:From D:\\Programing\\Anaconda3\\envs\\tf1_env\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
"\n",
"WARNING:tensorflow:From D:\\Programing\\Anaconda3\\envs\\tf1_env\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
"\n"
]
}
],
"source": [
"model = Sequential()\n",
"model.add(\n",
" Conv2D(2, (3, 3), padding='same', activation='relu', input_shape=(8, 8, 1)))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"model.add(Conv2D(3, (2, 2), padding='same', activation='relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"model.add(Flatten())\n",
"model.add(Dense(8, activation='relu'))\n",
"model.add(Dense(3, activation='softmax'))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 0 images belonging to 0 classes.\n"
]
}
],
"source": [
"from keras.preprocessing.image import ImageDataGenerator\n",
"train_datagen=ImageDataGenerator(rescale=1./255)\n",
"train_generator = train_datagen.flow_from_directory(\n",
" 'data_set/handwriting/train',\n",
" target_size=(24, 24),\n",
" batch_size=3,\n",
" class_mode='categorical')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:tf1_env]",
"language": "python",
"name": "conda-env-tf1_env-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}