Deep Learning Classification of Corn Leaf Disease.ipynb

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data from Kaggle"],"metadata":{"id":"jkG3txpydJA_"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qLj3zg6g4CaC","executionInfo":{"status":"ok","timestamp":1658974224185,"user_tz":180,"elapsed":25835,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}},"outputId":"926d0c9b-ecd8-4be6-88c6-ef7860ffa34e"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["!pip install kaggle"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IrcIFNXA33pf","executionInfo":{"status":"ok","timestamp":1658974229393,"user_tz":180,"elapsed":3393,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}},"outputId":"40904dbe-39b9-4cb2-e6e3-499a1cae4c36"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.64.0)\n","Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n","Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (6.1.2)\n","Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n","Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2022.6.15)\n","Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n"]}]},{"cell_type":"code","source":["!mkdir ~/.kaggle"],"metadata":{"id":"rT1wI2b34si4","executionInfo":{"status":"ok","timestamp":1658974231627,"user_tz":180,"elapsed":2,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["!cp /content/drive/MyDrive/CornLeafDisease/kaggle.json ~/.kaggle/"],"metadata":{"id":"ZwGlZ8yk42FC","executionInfo":{"status":"ok","timestamp":1658974232625,"user_tz":180,"elapsed":334,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["! chmod 600 ~/.kaggle/kaggle.json"],"metadata":{"id":"x9iMzd6N4-Vd","executionInfo":{"status":"ok","timestamp":1658974234114,"user_tz":180,"elapsed":6,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["!kaggle datasets download -d smaranjitghose/corn-or-maize-leaf-disease-dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qAUkN3Nh5Cp6","executionInfo":{"status":"ok","timestamp":1658974238766,"user_tz":180,"elapsed":2934,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}},"outputId":"628172f8-87b2-4e2b-b77f-9026f8dffaab"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading corn-or-maize-leaf-disease-dataset.zip to /content\n"," 88% 142M/161M [00:01<00:00, 108MB/s]\n","100% 161M/161M [00:01<00:00, 88.8MB/s]\n"]}]},{"cell_type":"markdown","source":["##Placeholder"],"metadata":{"id":"mqndfcrI_Oop"}},{"cell_type":"code","source":["  src_zip = \"corn-or-maize-leaf-disease-dataset.zip\"\n","  DATA_SRC = \"data\" # (technically a subset of only 10K images)\n","  IMAGES_PER_LABEL = 500\n","  BALANCED_SPLITS = {\"train\" : 400, \"val\" : 50, \"test\": 50}\n"],"metadata":{"id":"VpOlvrhwIs-V","executionInfo":{"status":"ok","timestamp":1658974240785,"user_tz":180,"elapsed":288,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["%%capture\n","!unzip $src_zip"],"metadata":{"id":"QZrmrmURRIW8","executionInfo":{"status":"ok","timestamp":1658974243927,"user_tz":180,"elapsed":1569,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}}},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":["##WandB"],"metadata":{"id":"rD1q3RrEJiUd"}},{"cell_type":"code","source":["!pip install wandb -qU\n","import wandb\n","wandb.login()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":213},"id":"LzNrrRwGJuX0","executionInfo":{"status":"ok","timestamp":1658974268140,"user_tz":180,"elapsed":22858,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}},"outputId":"d80aff8a-2a0b-4b1d-fa48-40e45be94005"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[K     |████████████████████████████████| 1.8 MB 28.2 MB/s \n","\u001b[K     |████████████████████████████████| 181 kB 55.8 MB/s \n","\u001b[K     |████████████████████████████████| 153 kB 76.5 MB/s \n","\u001b[K     |████████████████████████████████| 63 kB 1.9 MB/s \n","\u001b[?25h  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["\n","        window._wandbApiKey = new Promise((resolve, reject) => {\n","            function loadScript(url) {\n","            return new Promise(function(resolve, reject) {\n","                let newScript = document.createElement(\"script\");\n","                newScript.onerror = reject;\n","                newScript.onload = resolve;\n","                document.body.appendChild(newScript);\n","                newScript.src = url;\n","            });\n","            }\n","            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n","            const iframe = document.createElement('iframe')\n","            iframe.style.cssText = \"width:0;height:0;border:none\"\n","            document.body.appendChild(iframe)\n","            const handshake = new Postmate({\n","                container: iframe,\n","                url: 'https://wandb.ai/authorize'\n","            });\n","            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n","            handshake.then(function(child) {\n","                child.on('authorize', data => {\n","                    clearTimeout(timeout)\n","                    resolve(data)\n","                });\n","            });\n","            })\n","        });\n","    "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n","\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n","wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit: "]},{"name":"stdout","output_type":"stream","text":["··········\n"]},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"]},{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["import os\n","from random import shuffle\n","import numpy as np\n","\n","# source directory for all raw data\n","SRC = DATA_SRC\n","PREFIX = \"cornset\" # convenient for tracking local data\n","PROJECT_NAME = \"corn_leaf_photos\"\n","\n","# number of images per class label\n","# the total number of images is 4X this (4 classes)\n","TOTAL_IMAGES = IMAGES_PER_LABEL * 4"],"metadata":{"id":"M-m_2A8JJuuc","executionInfo":{"status":"ok","timestamp":1658974273737,"user_tz":180,"elapsed":434,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["labels = os.listdir(SRC)"],"metadata":{"id":"4zK2n1glk3fg","executionInfo":{"status":"ok","timestamp":1658974275825,"user_tz":180,"elapsed":2,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["# if this is a substantially new dataset, give it a new name\n","# this will create a whole new placeholder (Artifact) for this dataset\n","# instead of just incrementing a version of the old dataset\n","RAW_DATA_AT = \"_\".join([PREFIX, \"raw_data\", str(TOTAL_IMAGES)])\n","run = wandb.init(project=PROJECT_NAME, job_type=\"upload\")\n","# create an artifact for all the raw data\n","raw_data_at = wandb.Artifact(RAW_DATA_AT, type=\"raw_data\")\n","\n","# SRC_DIR contains 10 folders, one for each of 10 class labels\n","# each folder contains images of the corresponding class\n","labels = os.listdir(SRC)\n","for l in labels:\n","  imgs_per_label = os.path.join(SRC, l)\n","  if os.path.isdir(imgs_per_label):\n","    # filter out \"DS_Store\"\n","    imgs = [i for i in os.listdir(imgs_per_label) if not i.startswith(\".DS\")]\n","    # randomize the order\n","    shuffle(imgs)\n","    img_file_ids = imgs[:IMAGES_PER_LABEL]\n","    for f in img_file_ids:\n","      file_path = os.path.join(SRC, l, f)\n","      # add file to artifact by full path\n","      raw_data_at.add_file(file_path, name=l + \"/\" + f)\n","\n","# save artifact to W&B\n","run.log_artifact(raw_data_at)\n","run.finish()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175,"referenced_widgets":["35cac2aeb14449019d5f92f8b6647710","54e17332f5924da98245c0d3bf66af00","92ce474d5d8a4e1fa26c972a61063372","59f4b77cebd849b396d5306d8581777d","bbbad53ca86347a19b576a56a45178c3","7f3ffbcaa2dd4b21b2ecc087de13287c","179b83b8fe41421a91f04c80bcea882d","92cb158ad36f493b8f7f9c9ea571a417"]},"id":"y8Xr-MnoK-lC","executionInfo":{"status":"ok","timestamp":1658974317867,"user_tz":180,"elapsed":40546,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}},"outputId":"1458595d-dee0-42fe-9115-14eceb065cb4"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mjordaocassiano\u001b[0m (\u001b[33mgabrieljordao\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Tracking run with wandb version 0.12.21"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Run data is saved locally in <code>/content/wandb/run-20220728_021117-1a8unfvz</code>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Syncing run <strong><a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos/runs/1a8unfvz\" target=\"_blank\">earnest-bush-48</a></strong> to <a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["VBox(children=(Label(value='70.722 MB of 70.722 MB uploaded (37.409 MB deduped)\\r'), FloatProgress(value=1.0, …"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"35cac2aeb14449019d5f92f8b6647710"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["W&B sync reduced upload amount by 52.9%             "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Synced <strong style=\"color:#cdcd00\">earnest-bush-48</strong>: <a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos/runs/1a8unfvz\" target=\"_blank\">https://wandb.ai/gabrieljordao/corn_leaf_photos/runs/1a8unfvz</a><br/>Synced 4 W&B file(s), 0 media file(s), 2002 artifact file(s) and 1 other file(s)"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Find logs at: <code>./wandb/run-20220728_021117-1a8unfvz/logs</code>"]},"metadata":{}}]},{"cell_type":"markdown","source":["##Split raw data to prepare for training"],"metadata":{"id":"wPFwaLW18LTL"}},{"cell_type":"code","source":["# if this is a substantially different dataset, give it a new name\n","# this will create a whole new placeholder (Artifact) for this split\n","# instead of just incrementing a version of the old data split\n","SPLIT_DATA_AT = \"_\".join([PREFIX, \"80-10-10\", str(TOTAL_IMAGES)])\n","run = wandb.init(project=PROJECT_NAME, job_type=\"data_split\")\n","\n","# create balanced train, val, test splits\n","# each count is the number of images per label\n","SPLIT_COUNTS = {\"train\" : 400, \"val\" : 50, \"test\": 50}\n","\n","# find the most recent (\"latest\") version of the full raw data\n","# you can of course pass around programmatic aliases and not string literals\n","# note: RAW_DATA_AT is defined in the previous cell—if you're running\n","# just this step, you may need to hardcode it\n","data_at = run.use_artifact(RAW_DATA_AT + \":latest\")\n","# download it locally (for illustration purposes/across hardware; you can\n","# also sync/version artifacts by reference)\n","data_dir = data_at.download()\n","\n","data_split_at = wandb.Artifact(SPLIT_DATA_AT, type=\"balanced_data\")\n","\n","# create a table with columns we want to track/compare\n","preview_dt = wandb.Table(columns=[\"id\", \"image\", \"label\", \"split\"])\n","\n","labels = os.listdir(data_dir)\n","for l in labels:\n","  if l.startswith(\".\"): # skip non-label file\n","    continue\n","  imgs_per_label = os.listdir(os.path.join(data_dir, l))\n","  shuffle(imgs_per_label)\n","  start_id = 0\n","  for split, count in SPLIT_COUNTS.items():\n","    # take a subset\n","    split_imgs = imgs_per_label[start_id:start_id+count]\n","    for img_file in split_imgs:\n","      f_id = img_file.split(\".\")[0]\n","      full_path = os.path.join(data_dir, l, img_file)\n","      # add file to artifact by full path\n","      # note: pass the label to the name parameter to retain it in\n","      # the data structure \n","      data_split_at.add_file(full_path, name = os.path.join(split, l, img_file))\n","      # add a preview of the image\n","      if split != \"test\":\n","        preview_dt.add_data(f_id, wandb.Image(full_path), l, split)\n","      else:\n","        # pretend we have unlabeled test data\n","        # (replace \"unknown\" with l if you'd like to keep the labels :)\n","        preview_dt.add_data(f_id, wandb.Image(full_path), l, split)\n","    start_id += count\n","\n","# log artifact to W&B\n","data_split_at.add(preview_dt, \"data_split\")\n","run.log_artifact(data_split_at)\n","run.finish()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175,"referenced_widgets":["318822e0f9454f26b39806c370c5b359","3bdc163df6f14957802a28d81b3ed122","c0980f0b289e4f89bc7ead69f26fa87f","f46d912a1bf4438781040c26e88f4011","fb1d2e6225114c91b0743da90c3e58ac","68b96502422b43988d2b06e001b102b6","39fe55e411394c35b72df2ea55230135","dcd40405bfcc458aa96d55315936bda3"]},"id":"KH6_ml6P8L0d","executionInfo":{"status":"ok","timestamp":1658974376094,"user_tz":180,"elapsed":41996,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}},"outputId":"2c452174-42b3-439f-b542-12928e512cca"},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Tracking run with wandb version 0.12.21"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Run data is saved locally in <code>/content/wandb/run-20220728_021214-2usbi1wl</code>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Syncing run <strong><a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos/runs/2usbi1wl\" target=\"_blank\">apricot-elevator-49</a></strong> to <a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact cornset_raw_data_2000:latest, 70.66MB. 2000 files... Done. 0:0:0.9\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["VBox(children=(Label(value='71.198 MB of 71.198 MB uploaded (37.409 MB deduped)\\r'), FloatProgress(value=1.0, …"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"318822e0f9454f26b39806c370c5b359"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["W&B sync reduced upload amount by 52.5%             "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Synced <strong style=\"color:#cdcd00\">apricot-elevator-49</strong>: <a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos/runs/2usbi1wl\" target=\"_blank\">https://wandb.ai/gabrieljordao/corn_leaf_photos/runs/2usbi1wl</a><br/>Synced 5 W&B file(s), 0 media file(s), 2003 artifact file(s) and 1 other file(s)"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Find logs at: <code>./wandb/run-20220728_021214-2usbi1wl/logs</code>"]},"metadata":{}}]},{"cell_type":"markdown","source":["## Train Model"],"metadata":{"id":"SDO3Py1HyH0K"}},{"cell_type":"code","source":["# EXPERIMENT CONFIG\n","#------------------------\n","# Core globals to modify\n","NUM_EPOCHS = 5 \n","\n","\n","# optional globals to modify\n","# set to a custom name to help keep your experiments organized\n","RUN_NAME = \"\" \n","# change this if you'd like start a new set of comparable Tables\n","# (only Tables logged to the same key can be compared)\n","VAL_TABLE_NAME = \"predictions\" \n","\n","# hyperparams set low for demo/training speed\n","# if you set these higher, be mindful of how many items are in\n","# the dataset artifacts you chose by setting the SIZE at the top\n","NUM_TRAIN = BALANCED_SPLITS[\"train\"]*4\n","NUM_VAL = BALANCED_SPLITS[\"val\"]*4\n","\n","# enforced max for this is ceil(NUM_VAL/batch_size)\n","NUM_LOG_BATCHES = 16\n","\n","# ARTIFACTS CONFIG\n","#------------------------\n","# training data artifact to load\n","TRAIN_DATA_AT = PREFIX + \"_80-10-10_\" + str(TOTAL_IMAGES)\n","\n","# model name\n","# if you want to train a sufficiently different model, give this a new name\n","# to start a new lineage for the model, instead of just incrementing the\n","# version of the old model\n","MODEL_NAME = \"iv3_finetuned\"\n","\n","# folder in which to save the final, trained model\n","# if you want to train a sufficiently different model, give this a new name\n","# to start a new lineage for the model, instead of just incrementing the\n","# version of the old model\n","SAVE_MODEL_DIR = \"finetune_iv3_keras\"\n","\n","from tensorflow.keras.applications.inception_v3 import InceptionV3\n","from tensorflow.keras.callbacks import Callback\n","from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","from wandb.keras import WandbCallback\n","\n","# experiment configuration saved to W&B\n","CFG = {\n","  \"num_train\" : NUM_TRAIN,\n","  \"num_val\" : NUM_VAL,\n","  \"num_classes\" : 4,\n","  \"fc_size\" : 1024,\n","  \"epochs\" : NUM_EPOCHS,\n","  \"batch_size\" : 32,\n","\n","  # inceptionV3 settings\n","  \"img_width\" : 299,\n","  \"img_height\": 299\n","}\n","\n","# number of validation data batches to log/use when computing metrics\n","# at the end of each epoch\n","max_log_batches = int(np.ceil(float(CFG[\"num_val\"])/float(CFG[\"batch_size\"])))\n","# change this min to max to log ALL the available images to a Table\n","CFG[\"num_log_batches\"] = min(max_log_batches, NUM_LOG_BATCHES)\n","\n","def finetune_inception_model(fc_size, num_classes):\n","  \"\"\"Load InceptionV3 with ImageNet weights, freeze it,\n","  and attach a finetuning top for this classification task\"\"\"\n","  # load InceptionV3 as base\n","  base = InceptionV3(weights=\"imagenet\", include_top=\"False\")\n","  # freeze base layers\n","  for layer in base.layers:\n","    layer.trainable = False\n","  x = base.get_layer('mixed10').output \n","\n","  # attach a fine-tuning layer\n","  x = GlobalAveragePooling2D()(x)\n","  x = Dense(fc_size, activation='relu')(x)\n","  guesses = Dense(num_classes, activation='softmax')(x)\n","\n","  model = Model(inputs=base.input, outputs=guesses)\n","  model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n","  return model\n","\n","def train():\n","  \"\"\" Main training loop which freezes the InceptionV3 layers of the model\n","  and only trains the new top layers on the new data. A subsequent training\n","  phase might unfreeze all the layers and finetune the whole model on the new data\"\"\" \n","  run = wandb.init(project=PROJECT_NAME, name=RUN_NAME, job_type=\"train\", config=CFG)\n","  cfg = wandb.config\n","\n","  # locate and download training and validation data\n","  data_at = TRAIN_DATA_AT + \":latest\"\n","  data = run.use_artifact(data_at, type=\"balanced_data\")\n","  data_dir = data.download()\n","  train_dir = os.path.join(data_dir, \"train\")\n","  val_dir = os.path.join(data_dir, \"val\")\n","\n","  # create train and validation data generators\n","  train_datagen = ImageDataGenerator(\n","      rescale=1. / 255,\n","      shear_range=0.2,\n","      zoom_range=0.2,\n","      horizontal_flip=True)\n","  val_datagen = ImageDataGenerator(rescale=1. / 255)\n","\n","  train_generator = train_datagen.flow_from_directory(\n","    train_dir,\n","    target_size=(cfg.img_width, cfg.img_height),\n","    batch_size=cfg.batch_size,\n","    class_mode='categorical')\n","\n","  val_generator = val_datagen.flow_from_directory(\n","    val_dir,\n","    target_size=(cfg.img_width, cfg.img_height),\n","    batch_size=cfg.batch_size,\n","    class_mode='categorical',\n","    shuffle=False)\n","\n","  # instantiate model and callbacks\n","  model = finetune_inception_model(cfg.fc_size, cfg.num_classes)\n","  callbacks = [WandbCallback(), ValLog(val_generator, cfg.num_log_batches)]\n","\n","  # train!\n","  model.fit(\n","    train_generator,\n","    steps_per_epoch = cfg.num_train // cfg.batch_size,\n","    epochs=cfg.epochs,\n","    validation_data=val_generator,\n","    callbacks = callbacks,\n","    validation_steps = cfg.num_val // cfg.batch_size)\n","\n","  # save trained model as artifact\n","  trained_model_artifact = wandb.Artifact(\n","            MODEL_NAME, type=\"model\",\n","            description=\"finetuned inception v3\",\n","            metadata=dict(cfg))\n","  \n","  model.save(SAVE_MODEL_DIR)\n","  trained_model_artifact.add_dir(SAVE_MODEL_DIR)\n","  run.log_artifact(trained_model_artifact)\n","  run.finish()\n","\n","class ValLog(Callback):\n","  \"\"\" Custom callback to log validation images\n","  at the end of each training epoch\"\"\"\n","  def __init__(self, generator=None, num_log_batches=1):\n","    self.generator = generator\n","    self.num_batches = num_log_batches\n","    # store full names of classes\n","    self.flat_class_names = [k for k, v in generator.class_indices.items()]\n","\n","  def on_epoch_end(self, epoch, logs={}):\n","    # collect validation data and ground truth labels from generator\n","    val_data, val_labels = zip(*(self.generator[i] for i in range(self.num_batches)))\n","    val_data, val_labels = np.vstack(val_data), np.vstack(val_labels)\n","\n","    # use the trained model to generate predictions for the given number\n","    # of validation data batches (num_batches)\n","    val_preds = self.model.predict(val_data)\n","    true_ids = val_labels.argmax(axis=1)\n","    max_preds = val_preds.argmax(axis=1)\n","\n","    # log validation predictions alongside the run\n","    columns=[\"id\", \"image\", \"guess\", \"truth\"]\n","    for a in self.flat_class_names:\n","      columns.append(\"score_\" + a)\n","    predictions_table = wandb.Table(columns = columns)\n","    \n","    # log image, predicted and actual labels, and all scores\n","    for filepath, img, top_guess, scores, truth in zip(self.generator.filenames,\n","                                                       val_data, \n","                                                       max_preds, \n","                                                       val_preds,\n","                                                       true_ids):\n","      img_id = filepath.split('/')[-1].split(\".\")[0]\n","      row = [img_id, wandb.Image(img), \n","             self.flat_class_names[top_guess], self.flat_class_names[truth]]\n","      for s in scores.tolist():\n","        row.append(np.round(s, 4))\n","      predictions_table.add_data(*row)\n","    wandb.run.log({VAL_TABLE_NAME : predictions_table})"],"metadata":{"id":"wzm-2sAq8irL","executionInfo":{"status":"ok","timestamp":1658974385540,"user_tz":180,"elapsed":2685,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":838,"referenced_widgets":["655d8e649e734fe385251bd159f1f663","3df0019e21bb441b868a31e08951ed9f","389c310bf64043fe91d2f5814a1bef98","e003b6c467d3468bb5ca39a82aad3efe","059491edcf5a46dcae18d6a318f2dbec","cdc5f8df1c8c48499087533b267c3a95","02c09de799664703bc53d50324f330f5","755a244e91d74d4f905c4dcd0989288b"]},"id":"4C3XYOlN99Jr","executionInfo":{"status":"ok","timestamp":1658974710589,"user_tz":180,"elapsed":315710,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}},"outputId":"bbbe2959-cd3a-49d0-c865-e70219b91aca"},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Tracking run with wandb version 0.12.21"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Run data is saved locally in <code>/content/wandb/run-20220728_021314-3a4zwvld</code>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Syncing run <strong><a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos/runs/3a4zwvld\" target=\"_blank\">young-cherry-50</a></strong> to <a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact cornset_80-10-10_2000:latest, 71.13MB. 2001 files... Done. 0:0:0.9\n"]},{"output_type":"stream","name":"stdout","text":["Found 1600 images belonging to 4 classes.\n","Found 200 images belonging to 4 classes.\n","Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels.h5\n","96116736/96112376 [==============================] - 1s 0us/step\n","96124928/96112376 [==============================] - 1s 0us/step\n"]},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The save_model argument by default saves the model in the HDF5 format that cannot save custom objects like subclassed models and custom layers. This behavior will be deprecated in a future release in favor of the SavedModel format. Meanwhile, the HDF5 model is saved as W&B files and the SavedModel as W&B Artifacts.\n"]},{"output_type":"stream","name":"stdout","text":["WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/nn_ops.py:5214: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n","Epoch 1/5\n","50/50 [==============================] - 61s 933ms/step - loss: 0.7411 - accuracy: 0.7500 - val_loss: 0.3990 - val_accuracy: 0.7865 - _timestamp: 1658974457.0000 - _runtime: 63.0000\n","Epoch 2/5\n","50/50 [==============================] - 42s 836ms/step - loss: 0.2828 - accuracy: 0.8863 - val_loss: 0.4039 - val_accuracy: 0.8021 - _timestamp: 1658974503.0000 - _runtime: 109.0000\n","Epoch 3/5\n","50/50 [==============================] - 43s 868ms/step - loss: 0.2426 - accuracy: 0.9006 - val_loss: 0.3445 - val_accuracy: 0.8385 - _timestamp: 1658974544.0000 - _runtime: 150.0000\n","Epoch 4/5\n","50/50 [==============================] - 41s 818ms/step - loss: 0.2116 - accuracy: 0.9137 - val_loss: 0.3604 - val_accuracy: 0.8385 - _timestamp: 1658974587.0000 - _runtime: 193.0000\n","Epoch 5/5\n","50/50 [==============================] - 42s 836ms/step - loss: 0.2025 - accuracy: 0.9181 - val_loss: 0.4131 - val_accuracy: 0.8229 - _timestamp: 1658974627.0000 - _runtime: 233.0000\n","INFO:tensorflow:Assets written to: finetune_iv3_keras/assets\n"]},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./finetune_iv3_keras)... Done. 1.0s\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["VBox(children=(Label(value='243.785 MB of 243.785 MB uploaded (22.890 MB deduped)\\r'), FloatProgress(value=1.0…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"655d8e649e734fe385251bd159f1f663"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["W&B sync reduced upload amount by 9.4%             "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<style>\n","    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n","    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n","    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n","    </style>\n","<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>▁▇▇██</td></tr><tr><td>epoch</td><td>▁▃▅▆█</td></tr><tr><td>loss</td><td>█▂▂▁▁</td></tr><tr><td>val_accuracy</td><td>▁▃██▆</td></tr><tr><td>val_loss</td><td>▇▇▁▃█</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>GFLOPS</td><td>5.72554</td></tr><tr><td>accuracy</td><td>0.91812</td></tr><tr><td>best_epoch</td><td>2</td></tr><tr><td>best_val_loss</td><td>0.34445</td></tr><tr><td>epoch</td><td>4</td></tr><tr><td>loss</td><td>0.20249</td></tr><tr><td>val_accuracy</td><td>0.82292</td></tr><tr><td>val_loss</td><td>0.41312</td></tr></table><br/></div></div>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Synced <strong style=\"color:#cdcd00\">young-cherry-50</strong>: <a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos/runs/3a4zwvld\" target=\"_blank\">https://wandb.ai/gabrieljordao/corn_leaf_photos/runs/3a4zwvld</a><br/>Synced 5 W&B file(s), 6 media file(s), 211 artifact file(s) and 2 other file(s)"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Find logs at: <code>./wandb/run-20220728_021314-3a4zwvld/logs</code>"]},"metadata":{}}]},{"cell_type":"markdown","source":["## Test Model (Run Inference)\n","\n","\n"],"metadata":{"id":"TsyKEeanxS1c"}},{"cell_type":"code","source":["# optional globals to modify\n","# set to a custom name to help keep your experiments organized\n","RUN_NAME = \"\" \n","# change this if you'd like start a new set of comparable Tables\n","# (only Tables logged to the same key can be compared)\n","TEST_TABLE_NAME = \"test_results\" \n","\n","from tensorflow import keras\n","from tensorflow.keras.preprocessing import image\n","import numpy as np\n","import os\n","\n","MODEL_NAME = \"iv3_finetuned\"\n","# location of test data from our original split\n","# should match SPLIT_DATA_AT\n","TEST_DATA_AT = \"_\".join([PREFIX, \"80-10-10\", str(TOTAL_IMAGES)])\n","\n","run = wandb.init(project=PROJECT_NAME, job_type=\"inference\", name=RUN_NAME)\n","model_at = run.use_artifact(MODEL_NAME + \":latest\")\n","model_dir = model_at.download()\n","print(\"model: \", model_dir)\n","model = keras.models.load_model(model_dir)\n","model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n","\n","# download latest version of test data\n","test_data_at = run.use_artifact(TEST_DATA_AT + \":latest\")\n","test_dir = test_data_at.download()\n","test_dir += \"/test/\"\n","\n","#class_names = [\"Gray Leaf Spot\", \"Blight\", \"Common Rust\", \"Healthy\"]\n","#class_names = [\"Common Rust\", \"Gray Leaf Spot\", \"Blight\", \"Healthy\"]\n","class_names = [\"Blight\", \"Common Rust\",\"Gray Leaf Spot\", \"Healthy\"]\n","\n","# load test images\n","imgs = []\n","filenames = []\n","class_labels = os.listdir(test_dir)\n","truth = []\n","for l in class_labels:\n","  if l.startswith(\".\"):\n","    continue\n","  imgs_per_class = os.listdir(os.path.join(test_dir, l))\n","  for img in imgs_per_class:\n","    # track the image id\n","    filenames.append(img.split(\".\")[0])\n","    truth.append(l)\n","    img_path = os.path.join(test_dir, l, img)\n","    img = image.load_img(img_path, target_size=(299, 299))\n","    img = image.img_to_array(img)\n","    # don't forget to rescale test images to match the range of inputs\n","    # to the network\n","    img = np.expand_dims(img/255.0, axis=0)\n","    imgs.append(img)\n","\n","# predict on test data and bin predictions by guessed label \n","preds = {}\n","imgs = np.vstack(imgs)\n","classes = model.predict(imgs, batch_size=32)\n","for c in classes:\n","  class_id = np.argmax(c)\n","  if class_id in preds:\n","    preds[class_id] += 1\n","  else:\n","    preds[class_id] = 1\n","\n","# log inference results as a Table to the run workspace\n","columns=[\"id\", \"image\", \"guess\", \"truth\"]\n","for a in class_names:\n","  columns.append(\"score_\" + a)\n","test_dt = wandb.Table(columns = columns)\n","\n","# store all the scores for each image\n","for img_id, i, t, c in zip(filenames, imgs, truth, classes):\n","  guess = class_names[np.argmax(c)]\n","  row = [img_id, wandb.Image(i), guess, t]\n","  for c_i in c.tolist():\n","    row.append(np.round(c_i, 4))\n","  test_dt.add_data(*row)\n","  \n","run.log({TEST_TABLE_NAME : test_dt})\n","print(\"Quick distribution of predicted classes: \")\n","print(preds)\n","run.finish()"],"metadata":{"id":"SXABULR2xWJM","colab":{"base_uri":"https://localhost:8080/","height":333,"referenced_widgets":["b8c69b9e7fe54cbeac4ac7bec9ebb0d3","a1a85be0d2cf47028699186ff9125024","7ecfd3ac9bfa499dace022295d4eb02c","14e4eb8c37094c4198637155ad599793","2c46e3d948ea4265a927cfbdbf0fe7aa","05645363720449b5b2f4fbf38436bd48","fdb86bf580fb41909ed27a62786d3751","d173008c817d47b6aa0b5e02036be845","de31f01b00ec450d9968dcbd7712797c","f70560f3291c4c73b8d43a2ae08786f4","b08d1c1968da4b1d9532d3201bc932e5","baa6a14fd18545e9abc2c206bcbaadd0","3b532892f6704c3f929dcafd25dd257b","fc27189c9e3f4dc485d4dd88169520db","a87aa2508fce4471b66e89a68b9eb3cc","da55d7564d754b1ba426862f34d71aa4"]},"executionInfo":{"status":"ok","timestamp":1658974909297,"user_tz":180,"elapsed":40771,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}},"outputId":"2ec5d9c4-2fb2-446b-bf6a-71e92f28b02c"},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Finishing last run (ID:e99b6hoq) before initializing another..."]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["VBox(children=(Label(value='23.852 MB of 23.852 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, m…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b8c69b9e7fe54cbeac4ac7bec9ebb0d3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Synced <strong style=\"color:#cdcd00\">sleek-haze-52</strong>: <a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos/runs/e99b6hoq\" target=\"_blank\">https://wandb.ai/gabrieljordao/corn_leaf_photos/runs/e99b6hoq</a><br/>Synced 5 W&B file(s), 1 media file(s), 201 artifact file(s) and 0 other file(s)"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Find logs at: <code>./wandb/run-20220728_021944-e99b6hoq/logs</code>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Successfully finished last run (ID:e99b6hoq). Initializing new run:<br/>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Tracking run with wandb version 0.12.21"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Run data is saved locally in <code>/content/wandb/run-20220728_022108-wlagx8kj</code>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Syncing run <strong><a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos/runs/wlagx8kj\" target=\"_blank\">quiet-fog-53</a></strong> to <a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact iv3_finetuned:latest, 112.33MB. 4 files... Done. 0:0:0.1\n"]},{"output_type":"stream","name":"stdout","text":["model:  ./artifacts/iv3_finetuned:v18\n"]},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact cornset_80-10-10_2000:latest, 71.13MB. 2001 files... Done. 0:0:0.2\n"]},{"output_type":"stream","name":"stdout","text":["Quick distribution of predicted classes: \n","{2: 38, 0: 57, 1: 57, 3: 48}\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["VBox(children=(Label(value='23.950 MB of 23.950 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, m…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"de31f01b00ec450d9968dcbd7712797c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Synced <strong style=\"color:#cdcd00\">quiet-fog-53</strong>: <a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos/runs/wlagx8kj\" target=\"_blank\">https://wandb.ai/gabrieljordao/corn_leaf_photos/runs/wlagx8kj</a><br/>Synced 5 W&B file(s), 1 media file(s), 203 artifact file(s) and 1 other file(s)"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Find logs at: <code>./wandb/run-20220728_022108-wlagx8kj/logs</code>"]},"metadata":{}}]},{"cell_type":"markdown","source":["##Hyperparametric Tuning (Sweep)"],"metadata":{"id":"8MjPeokSxH-4"}},{"cell_type":"code","source":["# Configure the sweep – specify the parameters to search through, the search strategy, the optimization metric et all.\n","sweep_config = {\n","    'method': 'random', #grid, random\n","    'metric': {\n","      'name': 'accuracy',\n","      'goal': 'maximize',   \n","    },\n","    'parameters': {\n","        'batch_size': {\n","            'values': [32,64]\n","        },\n","        'epoch': {\n","            'values': [10,15,20]\n","        }, \n","        'criterion': {\n","            'values': [\"BinaryCrossentropy\", \"MeanSquaredError\"]\n","        },\n","         'optmizer': {\n","            'values': [\"sgd\", \"adam\", \"rmsprop\", \"adagrad\", \"MeanSquaredError\"]\n","        },\n","    }\n","}"],"metadata":{"id":"DVsSqQAX9Gry"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Initialize a new sweep\n","# Arguments:\n","#     – sweep_config: the sweep config dictionary defined above\n","#     – entity: Set the username for the sweep\n","#     – project: Set the project name for the sweep\n","sweep_id = wandb.sweep(sweep_config, entity=\"gabrieljordao\", project=\"corn_leaf_photos\")"],"metadata":{"id":"ZcrrV0dAuM0w","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1658928390112,"user_tz":180,"elapsed":1073,"user":{"displayName":"Jordao Cassiano","userId":"05793989457767983319"}},"outputId":"689a5923-d82a-4af1-9569-8154e12e54ce"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Create sweep with ID: hkwtdwts\n","Sweep URL: https://wandb.ai/gabrieljordao/corn_leaf_photos/sweeps/hkwtdwts\n"]}]},{"cell_type":"code","source":["# Initialize a new sweep\n","# Arguments:\n","#     – sweep_id: the sweep_id to run - this was returned above by wandb.sweep()\n","#     – function: function that defines your model architecture and trains it\n","wandb.agent(sweep_id = sweep_id, function=train, count=10)"],"metadata":{"id":"hzWp24ZUuNUk","colab":{"base_uri":"https://localhost:8080/","height":428},"outputId":"88dda5e3-07a9-4d38-f45a-d64dc34b8714"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: x995j10t with config:\n","\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n","\u001b[34m\u001b[1mwandb\u001b[0m: \tcriterion: MeanSquaredError\n","\u001b[34m\u001b[1mwandb\u001b[0m: \tepoch: 15\n","\u001b[34m\u001b[1mwandb\u001b[0m: \toptmizer: sgd\n","\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Ignored wandb.init() arg project when running a sweep.\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Tracking run with wandb version 0.12.21"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Run data is saved locally in <code>/content/wandb/run-20220727_132634-x995j10t</code>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Syncing run <strong><a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos/runs/x995j10t\" target=\"_blank\">zesty-sweep-1</a></strong> to <a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>Sweep page:  <a href=\"https://wandb.ai/gabrieljordao/corn_leaf_photos/sweeps/hkwtdwts\" target=\"_blank\">https://wandb.ai/gabrieljordao/corn_leaf_photos/sweeps/hkwtdwts</a>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact cornset_80-10-10_2000:latest, 91.09MB. 2001 files... Done. 0:0:1.1\n"]},{"output_type":"stream","name":"stdout","text":["Found 1600 images belonging to 4 classes.\n","Found 200 images belonging to 4 classes.\n","Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels.h5\n","96116736/96112376 [==============================] - 1s 0us/step\n","96124928/96112376 [==============================] - 1s 0us/step\n"]},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The save_model argument by default saves the model in the HDF5 format that cannot save custom objects like subclassed models and custom layers. This behavior will be deprecated in a future release in favor of the SavedModel format. Meanwhile, the HDF5 model is saved as W&B files and the SavedModel as W&B Artifacts.\n"]},{"output_type":"stream","name":"stdout","text":["WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/nn_ops.py:5214: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n","Epoch 1/5\n","35/50 [====================>.........] - ETA: 1:33 - loss: 0.8772 - accuracy: 0.6973"]}]}]}