Deep Learning Projects/deepfood-food-vision-big-cnn.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/kelixirr/deepfood-food-vision-big-cnn?scriptVersionId=144281037\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","source":"import tensorflow as tf","metadata":{"execution":{"iopub.status.busy":"2023-09-26T05:48:15.089385Z","iopub.execute_input":"2023-09-26T05:48:15.09038Z","iopub.status.idle":"2023-09-26T05:48:25.689307Z","shell.execute_reply.started":"2023-09-26T05:48:15.090332Z","shell.execute_reply":"2023-09-26T05:48:25.688174Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#importing helper function\n!wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py","metadata":{"execution":{"iopub.status.busy":"2023-09-26T05:48:25.691342Z","iopub.execute_input":"2023-09-26T05:48:25.692124Z","iopub.status.idle":"2023-09-26T05:48:26.919895Z","shell.execute_reply.started":"2023-09-26T05:48:25.692084Z","shell.execute_reply":"2023-09-26T05:48:26.918543Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow_datasets as tfds\n\ndatasets_list = tfds.list_builders()\n\ntarget_dataset = \"food101\"\nprint(f\"'{target_dataset}' in TensorFlow Datasets: {target_dataset in datasets_list}\")","metadata":{"execution":{"iopub.status.busy":"2023-09-26T05:48:26.922001Z","iopub.execute_input":"2023-09-26T05:48:26.922842Z","iopub.status.idle":"2023-09-26T05:48:30.419106Z","shell.execute_reply.started":"2023-09-26T05:48:26.922799Z","shell.execute_reply":"2023-09-26T05:48:30.417945Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# loading the data \n(train_data, test_data), ds_info = tfds.load(name = 'food101',\n                                            split = ['train', 'validation'],\n                                            shuffle_files = True,\n                                            as_supervised = True,\n                                            with_info = True)","metadata":{"execution":{"iopub.status.busy":"2023-09-26T05:48:30.422458Z","iopub.execute_input":"2023-09-26T05:48:30.422906Z","iopub.status.idle":"2023-09-26T06:01:23.808846Z","shell.execute_reply.started":"2023-09-26T05:48:30.422866Z","shell.execute_reply":"2023-09-26T06:01:23.806205Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ds_info.features","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:23.822589Z","iopub.execute_input":"2023-09-26T06:01:23.823072Z","iopub.status.idle":"2023-09-26T06:01:23.83584Z","shell.execute_reply.started":"2023-09-26T06:01:23.823027Z","shell.execute_reply":"2023-09-26T06:01:23.834549Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class_names = ds_info.features['label'].names\nclass_names[:10]","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:23.837668Z","iopub.execute_input":"2023-09-26T06:01:23.838318Z","iopub.status.idle":"2023-09-26T06:01:23.890752Z","shell.execute_reply.started":"2023-09-26T06:01:23.838281Z","shell.execute_reply":"2023-09-26T06:01:23.889534Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_one_sample = train_data.take(1)","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:23.893012Z","iopub.execute_input":"2023-09-26T06:01:23.893536Z","iopub.status.idle":"2023-09-26T06:01:23.910083Z","shell.execute_reply.started":"2023-09-26T06:01:23.893496Z","shell.execute_reply":"2023-09-26T06:01:23.908882Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_one_sample","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:23.912096Z","iopub.execute_input":"2023-09-26T06:01:23.91267Z","iopub.status.idle":"2023-09-26T06:01:23.923756Z","shell.execute_reply.started":"2023-09-26T06:01:23.91253Z","shell.execute_reply":"2023-09-26T06:01:23.922514Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for image, label in train_one_sample:\n    print(f\"\"\"\n    Image shape: {image.shape}\n    Image dtype: {image.dtype}\n    Target class from Food101 (tensor form): {label}\n    Class name (str form): {class_names[label.numpy()]}\n        \"\"\")","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:23.925524Z","iopub.execute_input":"2023-09-26T06:01:23.92611Z","iopub.status.idle":"2023-09-26T06:01:24.593831Z","shell.execute_reply.started":"2023-09-26T06:01:23.92607Z","shell.execute_reply":"2023-09-26T06:01:24.592661Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:24.599378Z","iopub.execute_input":"2023-09-26T06:01:24.599765Z","iopub.status.idle":"2023-09-26T06:01:24.611341Z","shell.execute_reply.started":"2023-09-26T06:01:24.599733Z","shell.execute_reply":"2023-09-26T06:01:24.609926Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# min and max values\ntf.reduce_min(image), tf.reduce_max(image)","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:24.613269Z","iopub.execute_input":"2023-09-26T06:01:24.614701Z","iopub.status.idle":"2023-09-26T06:01:24.644609Z","shell.execute_reply.started":"2023-09-26T06:01:24.614658Z","shell.execute_reply":"2023-09-26T06:01:24.643534Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt \nplt.imshow(image)\nplt.title(class_names[label])\nplt.axis()","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:24.646362Z","iopub.execute_input":"2023-09-26T06:01:24.647107Z","iopub.status.idle":"2023-09-26T06:01:25.237042Z","shell.execute_reply.started":"2023-09-26T06:01:24.64707Z","shell.execute_reply":"2023-09-26T06:01:25.236021Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Create Preprocessing Functions Of Our Data","metadata":{}},{"cell_type":"code","source":"image.shape, image.dtype","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:25.238204Z","iopub.execute_input":"2023-09-26T06:01:25.23863Z","iopub.status.idle":"2023-09-26T06:01:25.247833Z","shell.execute_reply.started":"2023-09-26T06:01:25.238592Z","shell.execute_reply":"2023-09-26T06:01:25.246687Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# prorcessing image\n\ndef preprocess_img(image, label, img_shape = 224):\n    \n    image = tf.image.resize(image, [img_shape, img_shape]) # reshaping image\n    return tf.cast(image, tf.float32), label","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:25.250059Z","iopub.execute_input":"2023-09-26T06:01:25.250977Z","iopub.status.idle":"2023-09-26T06:01:25.258786Z","shell.execute_reply.started":"2023-09-26T06:01:25.250938Z","shell.execute_reply":"2023-09-26T06:01:25.257527Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# checking our function \npreprocessed_img = preprocess_img(image, label)[0]\npreprocessed_img","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:25.261624Z","iopub.execute_input":"2023-09-26T06:01:25.262646Z","iopub.status.idle":"2023-09-26T06:01:25.296885Z","shell.execute_reply.started":"2023-09-26T06:01:25.262571Z","shell.execute_reply":"2023-09-26T06:01:25.295826Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(preprocessed_img/255.)\nplt.title(class_names[label])\nplt.axis(False);","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:25.298126Z","iopub.execute_input":"2023-09-26T06:01:25.298432Z","iopub.status.idle":"2023-09-26T06:01:25.646304Z","shell.execute_reply.started":"2023-09-26T06:01:25.298406Z","shell.execute_reply":"2023-09-26T06:01:25.645374Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# map the preprocessing image function \ntrain_data = train_data.map(map_func = preprocess_img, num_parallel_calls = tf.data.AUTOTUNE)\n\n# shuffle data \ntrain_data = train_data.shuffle(buffer_size = 1000).batch(batch_size = 32).prefetch(buffer_size = tf.data.AUTOTUNE)\n\n# Map prepreprocessing function to test data\ntest_data = test_data.map(preprocess_img, num_parallel_calls=tf.data.AUTOTUNE)\n\n# Turn test data into batches (don't need to shuffle)\ntest_data = test_data.batch(32).prefetch(tf.data.AUTOTUNE)","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:25.647873Z","iopub.execute_input":"2023-09-26T06:01:25.648951Z","iopub.status.idle":"2023-09-26T06:01:25.775284Z","shell.execute_reply.started":"2023-09-26T06:01:25.648913Z","shell.execute_reply":"2023-09-26T06:01:25.774205Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_data, test_data","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:25.776812Z","iopub.execute_input":"2023-09-26T06:01:25.777286Z","iopub.status.idle":"2023-09-26T06:01:25.786204Z","shell.execute_reply.started":"2023-09-26T06:01:25.777249Z","shell.execute_reply":"2023-09-26T06:01:25.785034Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# turning on mixed precision training for efficiency purpose\nfrom tensorflow.keras import mixed_precision\nmixed_precision.set_global_policy(policy = 'mixed_float16')","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:25.788163Z","iopub.execute_input":"2023-09-26T06:01:25.789002Z","iopub.status.idle":"2023-09-26T06:01:25.800564Z","shell.execute_reply.started":"2023-09-26T06:01:25.788963Z","shell.execute_reply":"2023-09-26T06:01:25.799518Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mixed_precision.global_policy()","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:25.802427Z","iopub.execute_input":"2023-09-26T06:01:25.8029Z","iopub.status.idle":"2023-09-26T06:01:25.811711Z","shell.execute_reply.started":"2023-09-26T06:01:25.802865Z","shell.execute_reply":"2023-09-26T06:01:25.810565Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Feature Extraction Model","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras import layers\n\n# create base model\ninput_shape = (224, 224, 3)\nbase_model = tf.keras.applications.EfficientNetB0(include_top=False)\nbase_model.trainable = False\n\n# function model \ninputs = layers.Input(shape = input_shape, name = \"input_layer\")\n\nx = base_model(inputs, training = False)\nx = layers.GlobalAveragePooling2D(name = 'pooling_layer')(x)\nx = layers.Dense(len(class_names))(x)\n\noutputs = layers.Activation(\"softmax\", dtype = tf.float32, name = 'softmax_float32')(x)\n\nmodel = tf.keras.Model(inputs, outputs)\n\n# compile the model \nmodel.compile(loss = 'sparse_categorical_crossentropy',\n              optimizer = tf.keras.optimizers.Adam(),\n              metrics = [\"accuracy\"])","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:25.813125Z","iopub.execute_input":"2023-09-26T06:01:25.814797Z","iopub.status.idle":"2023-09-26T06:01:30.178509Z","shell.execute_reply.started":"2023-09-26T06:01:25.814763Z","shell.execute_reply":"2023-09-26T06:01:30.177442Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:30.180263Z","iopub.execute_input":"2023-09-26T06:01:30.180673Z","iopub.status.idle":"2023-09-26T06:01:30.231919Z","shell.execute_reply.started":"2023-09-26T06:01:30.180637Z","shell.execute_reply":"2023-09-26T06:01:30.230932Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# checking for layers dtype policy\nfor layer in model.layers:\n    print(layer.name, layer.dtype_policy, layer.name, layer.dtype, layer.trainable)","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:30.233079Z","iopub.execute_input":"2023-09-26T06:01:30.234265Z","iopub.status.idle":"2023-09-26T06:01:30.240902Z","shell.execute_reply.started":"2023-09-26T06:01:30.234233Z","shell.execute_reply":"2023-09-26T06:01:30.239533Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for layer in model.layers[1].layers[:20]:\n    print(layer.name, layer.dtype_policy, layer.name, layer.dtype, layer.trainable)","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:30.242561Z","iopub.execute_input":"2023-09-26T06:01:30.24327Z","iopub.status.idle":"2023-09-26T06:01:30.256815Z","shell.execute_reply.started":"2023-09-26T06:01:30.243235Z","shell.execute_reply":"2023-09-26T06:01:30.255526Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# fit the model\nhistory_feature_extraction = model.fit(train_data,\n                                       epochs = 3,\n                                       steps_per_epoch = len(train_data),\n                                       validation_data = test_data,\n                                       validation_steps = int(0.15 * len(test_data)))","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:01:30.25867Z","iopub.execute_input":"2023-09-26T06:01:30.25911Z","iopub.status.idle":"2023-09-26T06:12:47.298956Z","shell.execute_reply.started":"2023-09-26T06:01:30.259076Z","shell.execute_reply":"2023-09-26T06:12:47.297688Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"results_feature_extract_model = model.evaluate(test_data)\nresults_feature_extract_model","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:12:47.304086Z","iopub.execute_input":"2023-09-26T06:12:47.304538Z","iopub.status.idle":"2023-09-26T06:13:44.666505Z","shell.execute_reply.started":"2023-09-26T06:12:47.304498Z","shell.execute_reply":"2023-09-26T06:13:44.66541Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"save_dir = \"Efficientnetb0_feature_extract_model_mixed_precision\"\nmodel.save(save_dir)","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:17:19.479591Z","iopub.execute_input":"2023-09-26T06:17:19.480898Z","iopub.status.idle":"2023-09-26T06:18:15.472629Z","shell.execute_reply.started":"2023-09-26T06:17:19.480835Z","shell.execute_reply":"2023-09-26T06:18:15.470952Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Please note: the above error is happening due to some bug in new version tensorflow. ","metadata":{}},{"cell_type":"code","source":"# Load model previously saved above\nloaded_saved_model = tf.keras.models.load_model(save_dir)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"results_loaded_saved_model = loaded_saved_model.evaluate(test_data)\nresults_loaded_saved_model","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":" #The loaded model's results should equal (or at least be very close) to the model's results prior to saving\nimport numpy as np\nassert np.isclose(results_feature_extract_model, results_loaded_saved_model).all()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Fine Tuning Our Model","metadata":{}},{"cell_type":"code","source":"# Download the saved model from Google Storage\n!wget https://storage.googleapis.com/ztm_tf_course/food_vision/07_efficientnetb0_feature_extract_model_mixed_precision.zip ","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:19:48.436929Z","iopub.execute_input":"2023-09-26T06:19:48.43735Z","iopub.status.idle":"2023-09-26T06:19:50.121029Z","shell.execute_reply.started":"2023-09-26T06:19:48.437319Z","shell.execute_reply":"2023-09-26T06:19:50.11971Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Unzip the SavedModel downloaded from Google Stroage\n!mkdir downloaded_gs_model # create new dir to store downloaded feature extraction model\n!unzip 07_efficientnetb0_feature_extract_model_mixed_precision.zip -d downloaded_gs_model","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:19:50.124531Z","iopub.execute_input":"2023-09-26T06:19:50.124972Z","iopub.status.idle":"2023-09-26T06:19:52.934056Z","shell.execute_reply.started":"2023-09-26T06:19:50.124932Z","shell.execute_reply":"2023-09-26T06:19:52.932586Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Load and evaluate downloaded GS model\nloaded_gs_model = tf.keras.models.load_model(\"downloaded_gs_model/07_efficientnetb0_feature_extract_model_mixed_precision\")","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:19:52.937453Z","iopub.execute_input":"2023-09-26T06:19:52.938999Z","iopub.status.idle":"2023-09-26T06:20:10.054875Z","shell.execute_reply.started":"2023-09-26T06:19:52.938878Z","shell.execute_reply":"2023-09-26T06:20:10.053716Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Get a summary of our downloaded model\nloaded_gs_model.summary()","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:20:10.064304Z","iopub.execute_input":"2023-09-26T06:20:10.064701Z","iopub.status.idle":"2023-09-26T06:20:10.128637Z","shell.execute_reply.started":"2023-09-26T06:20:10.064673Z","shell.execute_reply":"2023-09-26T06:20:10.127515Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# How does the loaded model perform?\nresults_loaded_gs_model = loaded_gs_model.evaluate(test_data)\nresults_loaded_gs_model","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:20:34.201656Z","iopub.execute_input":"2023-09-26T06:20:34.20207Z","iopub.status.idle":"2023-09-26T06:21:33.90355Z","shell.execute_reply.started":"2023-09-26T06:20:34.202037Z","shell.execute_reply":"2023-09-26T06:21:33.902441Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Are any of the layers in our model frozen?\nfor layer in loaded_gs_model.layers:\n    layer.trainable = True # set all layers to trainable\n    print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy)","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:21:33.907287Z","iopub.execute_input":"2023-09-26T06:21:33.907722Z","iopub.status.idle":"2023-09-26T06:21:33.933604Z","shell.execute_reply.started":"2023-09-26T06:21:33.90769Z","shell.execute_reply":"2023-09-26T06:21:33.932477Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for layer in loaded_gs_model.layers[1].layers[:20]:\n    print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy)","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:21:33.93637Z","iopub.execute_input":"2023-09-26T06:21:33.93757Z","iopub.status.idle":"2023-09-26T06:21:33.946342Z","shell.execute_reply.started":"2023-09-26T06:21:33.93753Z","shell.execute_reply":"2023-09-26T06:21:33.944828Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## creating callbacks for optimizing training time\nfrom helper_functions import create_tensorboard_callback\n\n#early stopping\nearly_stopping = tf.keras.callbacks.EarlyStopping(monitor = \"val_loss\",\n                                                  patience = 3) # if val loss decreases for 3 epochs in a row, stop training\n\n# model checkpoint\ncheckpoint_path = \"fine_tune_checkpoints/\"\nmodel_checkpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,\n                                                      save_best_only = True,\n                                                      monitor = 'val_loss')\n\n#learning rate\nreduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_loss\",\n                                                 factor = 0.2, # reduce lr by 5x\n                                                 patience = 2,\n                                                 verbose = 1,\n                                                 min_lr = 1e-7)","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:23:41.98637Z","iopub.execute_input":"2023-09-26T06:23:41.98756Z","iopub.status.idle":"2023-09-26T06:23:43.207962Z","shell.execute_reply.started":"2023-09-26T06:23:41.987509Z","shell.execute_reply":"2023-09-26T06:23:43.206751Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Compile the model\nloaded_gs_model.compile(loss=\"sparse_categorical_crossentropy\", # for labels that are not one-hot\n                        optimizer=tf.keras.optimizers.Adam(0.0001), # 10x lower learning rate \n                        metrics=[\"accuracy\"])","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:23:46.299598Z","iopub.execute_input":"2023-09-26T06:23:46.299983Z","iopub.status.idle":"2023-09-26T06:23:46.334153Z","shell.execute_reply.started":"2023-09-26T06:23:46.299954Z","shell.execute_reply":"2023-09-26T06:23:46.333153Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"history_fine_tuned_model = loaded_gs_model.fit(train_data,\n                                               epochs = 100,\n                                               steps_per_epoch = len(train_data),\n                                               validation_data = test_data,\n                                               validation_steps = int(0.15 * len(test_data)),\n                                               callbacks = [create_tensorboard_callback(\"training_logs\", \"efficientb0_101_classes_all_data_fine_tuning\"),\n                                                            model_checkpoint, # save only the best model during training\n                                                            early_stopping,\n                                                            reduce_lr]) ","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:39:42.872959Z","iopub.execute_input":"2023-09-26T06:39:42.873921Z","iopub.status.idle":"2023-09-26T07:05:32.825911Z","shell.execute_reply.started":"2023-09-26T06:39:42.873876Z","shell.execute_reply":"2023-09-26T07:05:32.823327Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# loaded_gs_model.save(\"07_efficientnetb0_fine_tuned_101_classes_mixed_precision\")  # not saving due to bug in Tf 10. above","metadata":{"execution":{"iopub.status.busy":"2023-09-26T06:14:42.360777Z","iopub.status.idle":"2023-09-26T06:14:42.361602Z","shell.execute_reply.started":"2023-09-26T06:14:42.361315Z","shell.execute_reply":"2023-09-26T06:14:42.361339Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Downloading the fine tuned model from Google Drive\n!wget https://storage.googleapis.com/ztm_tf_course/food_vision/07_efficientnetb0_fine_tuned_101_classes_mixed_precision.zip","metadata":{"execution":{"iopub.status.busy":"2023-09-26T07:08:26.130871Z","iopub.execute_input":"2023-09-26T07:08:26.132756Z","iopub.status.idle":"2023-09-26T07:08:28.068131Z","shell.execute_reply.started":"2023-09-26T07:08:26.132692Z","shell.execute_reply":"2023-09-26T07:08:28.066652Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Unzip fine-tuned model\n!mkdir downloaded_fine_tuned_gs_model # create separate directory for fine-tuned model downloaded from Google Storage\n!unzip 07_efficientnetb0_fine_tuned_101_classes_mixed_precision -d downloaded_fine_tuned_gs_model","metadata":{"execution":{"iopub.status.busy":"2023-09-26T07:08:30.599061Z","iopub.execute_input":"2023-09-26T07:08:30.600109Z","iopub.status.idle":"2023-09-26T07:08:33.979666Z","shell.execute_reply.started":"2023-09-26T07:08:30.600068Z","shell.execute_reply":"2023-09-26T07:08:33.978174Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Load in fine-tuned model from Google Storage and evaluate\nloaded_fine_tuned_gs_model = tf.keras.models.load_model(\"downloaded_fine_tuned_gs_model/07_efficientnetb0_fine_tuned_101_classes_mixed_precision\")","metadata":{"execution":{"iopub.status.busy":"2023-09-26T07:08:33.983907Z","iopub.execute_input":"2023-09-26T07:08:33.984236Z","iopub.status.idle":"2023-09-26T07:08:52.607904Z","shell.execute_reply.started":"2023-09-26T07:08:33.984208Z","shell.execute_reply":"2023-09-26T07:08:52.606686Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Get a model summary (same model architecture as above)\nloaded_fine_tuned_gs_model.summary()","metadata":{"execution":{"iopub.status.busy":"2023-09-26T07:08:52.773707Z","iopub.execute_input":"2023-09-26T07:08:52.774116Z","iopub.status.idle":"2023-09-26T07:08:52.897838Z","shell.execute_reply.started":"2023-09-26T07:08:52.774082Z","shell.execute_reply":"2023-09-26T07:08:52.895986Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"results_downloaded_fine_tuned_gs_model = loaded_fine_tuned_gs_model.evaluate(test_data)\nresults_downloaded_fine_tuned_gs_model","metadata":{"execution":{"iopub.status.busy":"2023-09-26T07:08:58.257816Z","iopub.execute_input":"2023-09-26T07:08:58.258277Z","iopub.status.idle":"2023-09-26T07:09:59.270656Z","shell.execute_reply.started":"2023-09-26T07:08:58.258245Z","shell.execute_reply":"2023-09-26T07:09:59.269528Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}