Modifs archi données
This commit is contained in:
Clémentine Bonneau
parent
4312087dc9
commit
fbea931fba
13 changed files with 76 additions and 63 deletions
139
Oiseau.ipynb
139
Oiseau.ipynb
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@ -46,7 +46,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -81,9 +81,31 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[name: \"/device:CPU:0\"\n",
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"device_type: \"CPU\"\n",
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"memory_limit: 268435456\n",
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"locality {\n",
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"}\n",
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"incarnation: 6212331521511793857\n",
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"]\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2021-12-22 20:34:12.391220: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations: SSE4.1 SSE4.2\n",
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"To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags.\n"
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]
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}
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],
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"source": [
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"from tensorflow.python.client import device_lib\n",
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"print(device_lib.list_local_devices())"
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@ -91,9 +113,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU\n"
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]
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}
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],
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"source": [
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"MODE = \"GPU\" if \"GPU\" in [k.device_type for k in device_lib.list_local_devices()] else \"CPU\"\n",
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"print(MODE)"
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@ -132,25 +162,24 @@
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"\n",
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"```\n",
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"data_dir\n",
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"└───sample/\n",
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"│ └───train/\n",
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"│ │ └───absence/\n",
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"│ │ │ │ absence_t_0001.jpg\n",
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"│ │ │ │ absence_t_0002.jpg\n",
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"│ │ │ │ ...\n",
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"│ │ └───presence/\n",
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"│ │ │ │ presence_t_0001.jpg\n",
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"│ │ │ │ presence_t_0002.jpg\n",
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"│ │ │ │ ...\n",
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"│ └───validation/\n",
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"│ │ └───absence/\n",
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"│ │ │ │ absence_v_0001.jpg\n",
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"│ │ │ │ absence_v_0002.jpg\n",
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"│ │ │ │ ...\n",
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"│ │ └───presence/\n",
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"│ │ │ │ presence_v_0001.jpg\n",
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"│ │ │ │ presence_v_0002.jpg\n",
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"│ │ │ │ ...\n",
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"└───train/\n",
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"│ └───absence/\n",
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"│ │ │ absence_t_0001.jpg\n",
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"│ │ │ absence_t_0002.jpg\n",
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"│ │ │ ...\n",
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"│ └───presence/\n",
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"│ │ │ presence_t_0001.jpg\n",
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"│ │ │ presence_t_0002.jpg\n",
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"│ │ │ ...\n",
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"└───validation/\n",
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"│ └───absence/\n",
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"│ │ │ absence_v_0001.jpg\n",
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"│ │ │ absence_v_0002.jpg\n",
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"│ │ │ ...\n",
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"│ └───presence/\n",
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"│ │ │ presence_v_0001.jpg\n",
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"│ │ │ presence_v_0002.jpg\n",
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"│ │ │ ...\n",
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"```"
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]
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},
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@ -158,23 +187,19 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Parameter"
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"### Reading the data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_dir = 'data/' # data path\n",
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"\n",
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"# Sample directory path\n",
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"# That way we can use this algorithm with an other sample if we have mire pictures in the future.\n",
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"\n",
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"N_train = 160\n",
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"N_val = 40\n",
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"data_dir_sub = data_dir+'sample_%d_Ntrain_%d_Nval' %(N_train, N_val)"
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"# That way we can use this algorithm with an other sample if we have mire pictures in the future.\n"
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]
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},
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{
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@ -188,23 +213,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'kpi' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/var/folders/83/bqq_97cs083fpgkn46bd5k5r0000gn/T/ipykernel_61636/3158407634.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkpi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_img\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_dir_sub\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'/train/presence/presence_t_0001.jpg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mNameError\u001b[0m: name 'kpi' is not defined"
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]
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}
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],
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"outputs": [],
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"source": [
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"img = kpi.load_img(data_dir_sub+'/train/presence/presence_t_0001.jpg') \n",
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"img = kpi.load_img(data_dir+'/train/presence/presence_t_0001.jpg') \n",
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"img"
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]
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},
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@ -281,15 +294,15 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"img = kpi.load_img(data_dir_sub+\"/train/presence/presence_t_0001.jpg\")\n",
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"img = kpi.load_img(data_dir+\"/train/presence/presence_t_0001.jpg\")\n",
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"x = kpi.img_to_array(img) \n",
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"x_ = np.expand_dims(x, axis=0)\n",
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"\n",
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"if not(os.path.isdir(data_dir_sub+\"/preprocessing_example\")):\n",
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" os.mkdir(data_dir_sub+\"/preprocessing_example\")\n",
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"if not(os.path.isdir(data_dir+\"/preprocessing_example\")):\n",
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" os.mkdir(data_dir+\"/preprocessing_example\")\n",
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"\n",
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" i = 0\n",
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" for batch in datagen.flow(x_, batch_size=1,save_to_dir=data_dir_sub+\"/preprocessing_example\", save_prefix='bird', save_format='jpg'):\n",
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" for batch in datagen.flow(x_, batch_size=1,save_to_dir=data_dir+\"/preprocessing_example\", save_prefix='bird', save_format='jpg'):\n",
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" i += 1\n",
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" if i > 7:\n",
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" break "
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@ -330,8 +343,8 @@
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],
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"source": [
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"X_list=[]\n",
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"for f in os.listdir(data_dir_sub+\"/preprocessing_example\"):\n",
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" X_list.append(kpi.img_to_array(kpi.load_img(data_dir_sub+\"/preprocessing_example/\"+f)))\n",
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"for f in os.listdir(data_dir+\"/preprocessing_example\"):\n",
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" X_list.append(kpi.img_to_array(kpi.load_img(data_dir+\"/preprocessing_example/\"+f)))\n",
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"\n",
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"\n",
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"fig=plt.figure(figsize=(16,8))\n",
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@ -411,8 +424,8 @@
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}
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],
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"source": [
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"x_0 = kpi.img_to_array(kpi.load_img(data_dir_sub+\"/train/presence/presence_t_0001.jpg\"))\n",
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"x_1 = kpi.img_to_array(kpi.load_img(data_dir_sub+\"/train/presence/presence_t_0002.jpg\"))\n",
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"x_0 = kpi.img_to_array(kpi.load_img(data_dir+\"/train/presence/presence_t_0001.jpg\"))\n",
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"x_1 = kpi.img_to_array(kpi.load_img(data_dir+\"/train/presence/presence_t_0002.jpg\"))\n",
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"x_0.shape, x_1.shape"
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]
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},
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@ -461,14 +474,14 @@
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"# subfolers of 'data/train', and indefinitely generate\n",
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"# batches of augmented image data\n",
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"train_generator = train_datagen.flow_from_directory(\n",
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" data_dir_sub+\"/train/\", # this is the target directory\n",
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" data_dir+\"/train/\", # this is the target directory\n",
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" target_size=(img_width, img_height), \n",
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" batch_size=batch_size,\n",
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" class_mode='binary') # since we use binary_crossentropy loss, we need binary labels\n",
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"\n",
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"# this is a similar generator, for validation data\n",
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"validation_generator = valid_datagen.flow_from_directory(\n",
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" data_dir_sub+\"/validation/\",\n",
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" data_dir+\"/validation/\",\n",
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" target_size=(img_width, img_height),\n",
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" batch_size=batch_size,\n",
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" class_mode='binary')"
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@ -840,7 +853,7 @@
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"datagen = kpi.ImageDataGenerator(rescale=1. / 255)\n",
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"\n",
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"generator = datagen.flow_from_directory(\n",
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" data_dir_sub+\"/train\",\n",
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" data_dir+\"/train\",\n",
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" target_size=(img_width, img_height),\n",
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" batch_size=batch_size,\n",
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" class_mode=None, # this means our generator will only yield batches of data, no labels\n",
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@ -849,7 +862,7 @@
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"\n",
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"\n",
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"generator = datagen.flow_from_directory(\n",
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" data_dir_sub+\"/validation\",\n",
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" data_dir+\"/validation\",\n",
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" target_size=(img_width, img_height),\n",
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" batch_size=batch_size,\n",
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" class_mode=None,\n",
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@ -1044,7 +1057,7 @@
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}
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],
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"source": [
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"model_VGG_fcm.save_weights(data_dir_sub+'/weights_model_VGG_fully_connected_model_%d_epochs_%d_batch_size.h5' %(epochs, batch_size))"
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"model_VGG_fcm.save_weights(data_dir+'/weights_model_VGG_fully_connected_model_%d_epochs_%d_batch_size.h5' %(epochs, batch_size))"
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]
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},
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{
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@ -1113,7 +1126,7 @@
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"top_model.add(kl.Dropout(0.5))\n",
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"top_model.add(kl.Dense(1, activation='sigmoid'))\n",
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"\n",
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"top_model.load_weights(data_dir_sub+'/weights_model_VGG_fully_connected_model_%d_epochs_%d_batch_size.h5' %(epochs, batch_size))\n"
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"top_model.load_weights(data_dir+'/weights_model_VGG_fully_connected_model_%d_epochs_%d_batch_size.h5' %(epochs, batch_size))\n"
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]
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},
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{
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@ -1247,13 +1260,13 @@
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"test_datagen = kpi.ImageDataGenerator(rescale=1. / 255)\n",
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"\n",
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"train_generator = train_datagen.flow_from_directory(\n",
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" data_dir_sub+\"/train/\",\n",
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" data_dir+\"/train/\",\n",
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" target_size=(img_height, img_width),\n",
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" batch_size=batch_size,\n",
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" class_mode='binary')\n",
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"\n",
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"validation_generator = test_datagen.flow_from_directory(\n",
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" data_dir_sub+\"/validation/\",\n",
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" data_dir+\"/validation/\",\n",
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" target_size=(img_height, img_width),\n",
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" batch_size=batch_size,\n",
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" class_mode='binary')\n"
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@ -1390,7 +1403,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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"version": "3.7.11"
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},
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"toc": {
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"nav_menu": {},
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