csd-mlflowpart.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":36420,"sourceType":"datasetVersion","datasetId":28577}],"dockerImageVersionId":30674,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install mlflow","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:02:23.871712Z","iopub.execute_input":"2024-04-01T16:02:23.872319Z","iopub.status.idle":"2024-04-01T16:02:40.460581Z","shell.execute_reply.started":"2024-04-01T16:02:23.872285Z","shell.execute_reply":"2024-04-01T16:02:40.459670Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Collecting mlflow\n  Downloading mlflow-2.11.3-py3-none-any.whl.metadata (15 kB)\nRequirement already satisfied: click<9,>=7.0 in /opt/conda/lib/python3.10/site-packages (from mlflow) (8.1.7)\nRequirement already satisfied: cloudpickle<4 in /opt/conda/lib/python3.10/site-packages (from mlflow) (2.2.1)\nRequirement already satisfied: entrypoints<1 in /opt/conda/lib/python3.10/site-packages (from mlflow) (0.4)\nRequirement already satisfied: gitpython<4,>=3.1.9 in /opt/conda/lib/python3.10/site-packages (from mlflow) (3.1.41)\nRequirement already satisfied: pyyaml<7,>=5.1 in /opt/conda/lib/python3.10/site-packages (from mlflow) (6.0.1)\nRequirement already satisfied: protobuf<5,>=3.12.0 in /opt/conda/lib/python3.10/site-packages (from mlflow) (3.20.3)\nRequirement already satisfied: pytz<2025 in /opt/conda/lib/python3.10/site-packages (from mlflow) (2023.3.post1)\nRequirement already satisfied: requests<3,>=2.17.3 in /opt/conda/lib/python3.10/site-packages (from mlflow) (2.31.0)\nRequirement already satisfied: packaging<24 in /opt/conda/lib/python3.10/site-packages (from mlflow) (21.3)\nRequirement already satisfied: importlib-metadata!=4.7.0,<8,>=3.7.0 in /opt/conda/lib/python3.10/site-packages (from mlflow) (6.11.0)\nRequirement already satisfied: sqlparse<1,>=0.4.0 in /opt/conda/lib/python3.10/site-packages (from mlflow) (0.4.4)\nRequirement already satisfied: alembic!=1.10.0,<2 in /opt/conda/lib/python3.10/site-packages (from mlflow) (1.13.1)\nRequirement already satisfied: docker<8,>=4.0.0 in /opt/conda/lib/python3.10/site-packages (from mlflow) (7.0.0)\nRequirement already satisfied: Flask<4 in 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satisfied: matplotlib<4 in /opt/conda/lib/python3.10/site-packages (from mlflow) (3.7.5)\nCollecting graphene<4 (from mlflow)\n  Downloading graphene-3.3-py2.py3-none-any.whl.metadata (7.7 kB)\nCollecting gunicorn<22 (from mlflow)\n  Downloading gunicorn-21.2.0-py3-none-any.whl.metadata (4.1 kB)\nRequirement already satisfied: Jinja2<4,>=2.11 in /opt/conda/lib/python3.10/site-packages (from mlflow) (3.1.2)\nRequirement already satisfied: Mako in /opt/conda/lib/python3.10/site-packages (from alembic!=1.10.0,<2->mlflow) (1.3.2)\nRequirement already satisfied: typing-extensions>=4 in /opt/conda/lib/python3.10/site-packages (from alembic!=1.10.0,<2->mlflow) (4.9.0)\nRequirement already satisfied: urllib3>=1.26.0 in /opt/conda/lib/python3.10/site-packages (from docker<8,>=4.0.0->mlflow) (1.26.18)\nRequirement already satisfied: Werkzeug>=3.0.0 in /opt/conda/lib/python3.10/site-packages (from Flask<4->mlflow) (3.0.1)\nRequirement already satisfied: itsdangerous>=2.1.2 in 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smmap<6,>=3.0.1 in /opt/conda/lib/python3.10/site-packages (from gitdb<5,>=4.0.1->gitpython<4,>=3.1.9->mlflow) (5.0.1)\nDownloading mlflow-2.11.3-py3-none-any.whl (19.7 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.7/19.7 MB\u001b[0m \u001b[31m71.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hDownloading graphene-3.3-py2.py3-none-any.whl (128 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m128.2/128.2 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading gunicorn-21.2.0-py3-none-any.whl (80 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.2/80.2 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading querystring_parser-1.2.4-py2.py3-none-any.whl (7.9 kB)\nDownloading aniso8601-9.0.1-py2.py3-none-any.whl (52 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.8/52.8 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading graphql_core-3.2.3-py3-none-any.whl (202 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m202.9/202.9 kB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading graphql_relay-3.2.0-py3-none-any.whl (16 kB)\nInstalling collected packages: aniso8601, querystring-parser, graphql-core, gunicorn, graphql-relay, graphene, mlflow\nSuccessfully installed aniso8601-9.0.1 graphene-3.3 graphql-core-3.2.3 graphql-relay-3.2.0 gunicorn-21.2.0 mlflow-2.11.3 querystring-parser-1.2.4\n","output_type":"stream"}]},{"cell_type":"code","source":"!pip install --upgrade tensorflow\n","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:02:40.462659Z","iopub.execute_input":"2024-04-01T16:02:40.463013Z","iopub.status.idle":"2024-04-01T16:03:47.684562Z","shell.execute_reply.started":"2024-04-01T16:02:40.462977Z","shell.execute_reply":"2024-04-01T16:03:47.683345Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Requirement already satisfied: tensorflow in /opt/conda/lib/python3.10/site-packages (2.15.0)\nCollecting tensorflow\n  Downloading tensorflow-2.16.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.3 kB)\nRequirement already satisfied: absl-py>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (1.4.0)\nRequirement already satisfied: astunparse>=1.6.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (1.6.3)\nRequirement already satisfied: flatbuffers>=23.5.26 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (23.5.26)\nRequirement already satisfied: 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\u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hDownloading ml_dtypes-0.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m60.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m\n\u001b[?25hDownloading tensorboard-2.16.2-py3-none-any.whl (5.5 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m94.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m\n\u001b[?25hInstalling collected packages: ml-dtypes, tensorboard, tensorflow\n  Attempting uninstall: ml-dtypes\n    Found existing installation: ml-dtypes 0.2.0\n    Uninstalling ml-dtypes-0.2.0:\n      Successfully uninstalled ml-dtypes-0.2.0\n  Attempting uninstall: tensorboard\n    Found existing installation: tensorboard 2.15.1\n    Uninstalling tensorboard-2.15.1:\n      Successfully uninstalled tensorboard-2.15.1\n  Attempting uninstall: tensorflow\n    Found existing installation: tensorflow 2.15.0\n    Uninstalling tensorflow-2.15.0:\n      Successfully uninstalled tensorflow-2.15.0\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\ntensorflow-decision-forests 1.8.1 requires wurlitzer, which is not installed.\ntensorflow-decision-forests 1.8.1 requires tensorflow~=2.15.0, but you have tensorflow 2.16.1 which is incompatible.\ntensorflow-text 2.15.0 requires tensorflow<2.16,>=2.15.0; platform_machine != \"arm64\" or platform_system != \"Darwin\", but you have tensorflow 2.16.1 which is incompatible.\ntf-keras 2.15.1 requires tensorflow<2.16,>=2.15, but you have tensorflow 2.16.1 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed ml-dtypes-0.3.2 tensorboard-2.16.2 tensorflow-2.16.1\n","output_type":"stream"}]},{"cell_type":"code","source":"import os\nimport numpy as np\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator \nfrom tensorflow.keras.optimizers import Adam \nfrom tensorflow.keras.models import Sequential, model_from_json  \nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout, BatchNormalization, Activation\nfrom tensorflow.keras.preprocessing.image import load_img, img_to_array\nfrom sklearn.model_selection import train_test_split\nfrom math import ceil\nfrom sklearn.preprocessing import LabelEncoder","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:03:47.685969Z","iopub.execute_input":"2024-04-01T16:03:47.686302Z","iopub.status.idle":"2024-04-01T16:03:55.309202Z","shell.execute_reply.started":"2024-04-01T16:03:47.686272Z","shell.execute_reply":"2024-04-01T16:03:55.308227Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.applications import ResNet50\nfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D\nfrom tensorflow.keras.models import Model\n\ndf = pd.read_csv('/kaggle/input/fer2013/fer2013.csv')\n\n\nX = np.array([np.fromstring(image, dtype=int, sep=' ') for image in df['pixels']])\nX = X.reshape(-1, 48, 48, 1)  # Reshape images to 48x48 grayscale\nX = np.concatenate([X, X, X], axis=-1)  # Convert grayscale to RGB\nX = X.astype('float32') / 255.0  # Normalize pixel values between 0 and 1\n\ny = pd.get_dummies(df['emotion']).values  # One-hot encode labels\n\n# Split the dataset into training, validation, and testing sets\nfrom sklearn.model_selection import train_test_split\nX_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\nX_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n\n# Define ResNet model with pre-trained weights (transfer learning)\nbase_model = ResNet50(weights='imagenet', include_top=False)\nx = base_model.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(1024, activation='relu')(x)\npredictions = Dense(7, activation='softmax')(x)\nmodel = Model(inputs=base_model.input, outputs=predictions)\n\n# Freeze pre-trained layers\nfor layer in base_model.layers:\n    layer.trainable = False\n\n# Compile the model\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n\n# Train the model\nmodel.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=5, batch_size=64)\n\n# Evaluate the model\nloss, accuracy = model.evaluate(X_test, y_test)\nprint(f'Test Loss: {loss}, Test Accuracy: {accuracy}')\n","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:03:55.311356Z","iopub.execute_input":"2024-04-01T16:03:55.311882Z","iopub.status.idle":"2024-04-01T16:05:11.413524Z","shell.execute_reply.started":"2024-04-01T16:03:55.311856Z","shell.execute_reply":"2024-04-01T16:05:11.412592Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n\u001b[1m94765736/94765736\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\nEpoch 1/5\n","output_type":"stream"},{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1711987458.560752     100 service.cc:145] XLA service 0x7ebdf004b180 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\nI0000 00:00:1711987458.560793     100 service.cc:153]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\nI0000 00:00:1711987458.560797     100 service.cc:153]   StreamExecutor device (1): Tesla T4, Compute Capability 7.5\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m  9/393\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 14ms/step - accuracy: 0.1451 - loss: 2.5324   ","output_type":"stream"},{"name":"stderr","text":"I0000 00:00:1711987463.186341     100 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 40ms/step - accuracy: 0.2232 - loss: 1.9102 - val_accuracy: 0.2554 - val_loss: 1.7845\nEpoch 2/5\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.2754 - loss: 1.7585 - val_accuracy: 0.2970 - val_loss: 1.7372\nEpoch 3/5\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.2859 - loss: 1.7382 - val_accuracy: 0.2826 - val_loss: 1.7546\nEpoch 4/5\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3032 - loss: 1.7225 - val_accuracy: 0.2409 - val_loss: 1.7977\nEpoch 5/5\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3128 - loss: 1.7036 - val_accuracy: 0.3149 - val_loss: 1.6877\n\u001b[1m169/169\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.2916 - loss: 1.7185\nTest Loss: 1.6946698427200317, Test Accuracy: 0.30572065711021423\n","output_type":"stream"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\n# Train the model and save the history\nhistory = model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=5, batch_size=64)\n\n# Plot training and validation loss\nplt.plot(history.history['loss'], label='Training Loss')\nplt.plot(history.history['val_loss'], label='Validation Loss')\nplt.xlabel('Epoch')\nplt.ylabel('Loss')\nplt.legend()\nplt.title('Training and Validation Loss')\nplt.show()\n\n# Plot training and validation accuracy\nplt.plot(history.history['accuracy'], label='Training Accuracy')\nplt.plot(history.history['val_accuracy'], label='Validation Accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.legend()\nplt.title('Training and Validation Accuracy')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:05:11.414803Z","iopub.execute_input":"2024-04-01T16:05:11.415138Z","iopub.status.idle":"2024-04-01T16:05:48.224606Z","shell.execute_reply.started":"2024-04-01T16:05:11.415114Z","shell.execute_reply":"2024-04-01T16:05:48.223641Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Epoch 1/5\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 18ms/step - accuracy: 0.3147 - loss: 1.6910 - val_accuracy: 0.3195 - val_loss: 1.6781\nEpoch 2/5\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3229 - loss: 1.6817 - val_accuracy: 0.3305 - val_loss: 1.6951\nEpoch 3/5\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3365 - loss: 1.6646 - val_accuracy: 0.3190 - val_loss: 1.6922\nEpoch 4/5\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3346 - loss: 1.6649 - val_accuracy: 0.3426 - val_loss: 1.6664\nEpoch 5/5\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3459 - loss: 1.6467 - val_accuracy: 0.3180 - val_loss: 1.6744\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}}]},{"cell_type":"code","source":"pip install databricks-sdk","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:05:48.226036Z","iopub.execute_input":"2024-04-01T16:05:48.226436Z","iopub.status.idle":"2024-04-01T16:06:01.564272Z","shell.execute_reply.started":"2024-04-01T16:05:48.226401Z","shell.execute_reply":"2024-04-01T16:06:01.563057Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"Collecting databricks-sdk\n  Downloading databricks_sdk-0.23.0-py3-none-any.whl.metadata (34 kB)\nRequirement already satisfied: google-auth~=2.0 in /opt/conda/lib/python3.10/site-packages (from databricks-sdk) (2.26.1)\nRequirement already satisfied: requests<3,>=2.28.1 in /opt/conda/lib/python3.10/site-packages (from databricks-sdk) (2.31.0)\nRequirement already satisfied: cachetools<6.0,>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from google-auth~=2.0->databricks-sdk) (4.2.4)\nRequirement already satisfied: pyasn1-modules>=0.2.1 in /opt/conda/lib/python3.10/site-packages (from google-auth~=2.0->databricks-sdk) (0.3.0)\nRequirement already satisfied: rsa<5,>=3.1.4 in /opt/conda/lib/python3.10/site-packages (from google-auth~=2.0->databricks-sdk) (4.9)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2.28.1->databricks-sdk) (3.3.2)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2.28.1->databricks-sdk) (3.6)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2.28.1->databricks-sdk) (1.26.18)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2.28.1->databricks-sdk) (2024.2.2)\nRequirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /opt/conda/lib/python3.10/site-packages (from pyasn1-modules>=0.2.1->google-auth~=2.0->databricks-sdk) (0.5.1)\nDownloading databricks_sdk-0.23.0-py3-none-any.whl (465 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m465.5/465.5 kB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n\u001b[?25hInstalling collected packages: databricks-sdk\nSuccessfully installed databricks-sdk-0.23.0\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:06:01.565949Z","iopub.execute_input":"2024-04-01T16:06:01.566357Z","iopub.status.idle":"2024-04-01T16:07:40.369131Z","shell.execute_reply.started":"2024-04-01T16:06:01.566319Z","shell.execute_reply":"2024-04-01T16:07:40.368209Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"2024/04/01 16:06:03 INFO mlflow.utils.credentials: No valid Databricks credentials found, please enter your credentials...\n","output_type":"stream"},{"output_type":"stream","name":"stdin","text":"Databricks Host (should begin with https://):  https://community.cloud.databricks.com/\nUsername:  chiruvolu@iitbhilai.ac.in\nPassword:  ·········\n"},{"name":"stderr","text":"2024/04/01 16:07:40 INFO mlflow.utils.credentials: Successfully connected to MLflow hosted tracking server! Host: https://community.cloud.databricks.com.\n","output_type":"stream"}]},{"cell_type":"code","source":"import mlflow\nimport mlflow.tensorflow\nfrom tensorflow.keras.callbacks import History\n\n# Set the MLflow experiment\nmlflow.set_experiment(\"/mlflow-tf-keras-resnet\")\n\n# Log parameters\nmlflow.log_param(\"epochs\", 5)\n\n# Train the model and log metrics\nhistory = History()\nmodel.fit(x=X_train, y=y_train, epochs=5, validation_data=(X_val, y_val), callbacks=[history])\n\n# Log metrics\nfor epoch, loss in enumerate(history.history['loss'], 1):\n    mlflow.log_metric(\"train_loss_epoch_\" + str(epoch), loss)\nfor epoch, accuracy in enumerate(history.history['accuracy'], 1):\n    mlflow.log_metric(\"train_accuracy_epoch_\" + str(epoch), accuracy)\nfor epoch, val_loss in enumerate(history.history['val_loss'], 1):\n    mlflow.log_metric(\"val_loss_epoch_\" + str(epoch), val_loss)\nfor epoch, val_accuracy in enumerate(history.history['val_accuracy'], 1):\n    mlflow.log_metric(\"val_accuracy_epoch_\" + str(epoch), val_accuracy)\n\n# End the MLflow run\nmlflow.end_run()\n","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:07:40.370539Z","iopub.execute_input":"2024-04-01T16:07:40.370856Z","iopub.status.idle":"2024-04-01T16:08:36.868962Z","shell.execute_reply.started":"2024-04-01T16:07:40.370830Z","shell.execute_reply":"2024-04-01T16:08:36.868136Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"Epoch 1/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 14ms/step - accuracy: 0.3306 - loss: 1.6728 - val_accuracy: 0.3463 - val_loss: 1.6671\nEpoch 2/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 13ms/step - accuracy: 0.3282 - loss: 1.6762 - val_accuracy: 0.3095 - val_loss: 1.7091\nEpoch 3/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 13ms/step - accuracy: 0.3358 - loss: 1.6614 - val_accuracy: 0.3474 - val_loss: 1.6647\nEpoch 4/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 13ms/step - accuracy: 0.3459 - loss: 1.6514 - val_accuracy: 0.3517 - val_loss: 1.6563\nEpoch 5/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 13ms/step - accuracy: 0.3458 - loss: 1.6561 - val_accuracy: 0.3398 - val_loss: 1.6782\n","output_type":"stream"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport mlflow\nimport mlflow.tensorflow\nfrom tensorflow.keras.callbacks import History\nimport os\n\n# Set the MLflow experiment\nmlflow.set_experiment(\"/mlflow-tf-keras-resnet\")\n\n# Log parameters\nmlflow.log_param(\"epochs\", 5)\n\n# Train the model and log metrics\nhistory = History()\nmodel.fit(x=X_train, y=y_train, epochs=5, validation_data=(X_val, y_val), callbacks=[history])\n\n# Log metrics\nfor epoch, loss in enumerate(history.history['loss'], 1):\n    mlflow.log_metric(\"train_loss_epoch_\" + str(epoch), loss)\nfor epoch, accuracy in enumerate(history.history['accuracy'], 1):\n    mlflow.log_metric(\"train_accuracy_epoch_\" + str(epoch), accuracy)\nfor epoch, val_loss in enumerate(history.history['val_loss'], 1):\n    mlflow.log_metric(\"val_loss_epoch_\" + str(epoch), val_loss)\nfor epoch, val_accuracy in enumerate(history.history['val_accuracy'], 1):\n    mlflow.log_metric(\"val_accuracy_epoch_\" + str(epoch), val_accuracy)\n\n# Save example images to a directory\nexample_images_dir = 'example_images'\nos.makedirs(example_images_dir, exist_ok=True)\nfor i in range(5):  # Save 5 example images\n    image_path = os.path.join(example_images_dir, f\"example_image_{i}.png\")\n    # Assuming X_train contains images in numpy array format\n    image = X_train[i] * 255.0  # Denormalize the image\n    image = image.astype('uint8')  # Convert to uint8\n    # Save the image\n    plt.imsave(image_path, image.squeeze(), cmap='gray')\n\n# Log example images as artifacts\nmlflow.log_artifacts(example_images_dir, artifact_path=\"example_images\")\n\n# End the MLflow run\nmlflow.end_run()\n","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:08:36.870310Z","iopub.execute_input":"2024-04-01T16:08:36.870676Z","iopub.status.idle":"2024-04-01T16:09:33.341540Z","shell.execute_reply.started":"2024-04-01T16:08:36.870642Z","shell.execute_reply":"2024-04-01T16:09:33.340679Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Epoch 1/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - accuracy: 0.3435 - loss: 1.6447 - val_accuracy: 0.3485 - val_loss: 1.6404\nEpoch 2/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 13ms/step - accuracy: 0.3526 - loss: 1.6344 - val_accuracy: 0.3299 - val_loss: 1.6610\nEpoch 3/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 13ms/step - accuracy: 0.3587 - loss: 1.6308 - val_accuracy: 0.3418 - val_loss: 1.6444\nEpoch 4/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 13ms/step - accuracy: 0.3566 - loss: 1.6283 - val_accuracy: 0.3413 - val_loss: 1.6500\nEpoch 5/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 13ms/step - accuracy: 0.3586 - loss: 1.6224 - val_accuracy: 0.3440 - val_loss: 1.6486\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Uploading artifacts:   0%|          | 0/5 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b9c883e5a508455686b54ac6853fd525"}},"metadata":{}}]},{"cell_type":"code","source":"from tensorflow.keras.applications import ResNet50\nfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D\nfrom tensorflow.keras.models import Model\n\n# Define ResNet model with pre-trained weights (transfer learning)\nbase_model = ResNet50(weights='imagenet', include_top=False)\nx = base_model.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(2048, activation='relu')(x)  # Increased units in Dense layer\npredictions = Dense(7, activation='softmax')(x)\nmodel_variation_1 = Model(inputs=base_model.input, outputs=predictions)\n\n# Freeze pre-trained layers\nfor layer in base_model.layers:\n    layer.trainable = False\n\n# Compile the model\nmodel_variation_1.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n\n# Train the model\nmodel_variation_1.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=5, batch_size=32)  # Decreased batch size\n","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:09:33.345303Z","iopub.execute_input":"2024-04-01T16:09:33.345824Z","iopub.status.idle":"2024-04-01T16:10:43.422955Z","shell.execute_reply.started":"2024-04-01T16:09:33.345796Z","shell.execute_reply":"2024-04-01T16:10:43.421927Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Epoch 1/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 20ms/step - accuracy: 0.2219 - loss: 1.9490 - val_accuracy: 0.2586 - val_loss: 1.8186\nEpoch 2/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - accuracy: 0.2712 - loss: 1.7642 - val_accuracy: 0.3030 - val_loss: 1.7283\nEpoch 3/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 14ms/step - accuracy: 0.2917 - loss: 1.7393 - val_accuracy: 0.3004 - val_loss: 1.7344\nEpoch 4/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 14ms/step - accuracy: 0.3018 - loss: 1.7178 - val_accuracy: 0.3240 - val_loss: 1.6826\nEpoch 5/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - accuracy: 0.3239 - loss: 1.6896 - val_accuracy: 0.3227 - val_loss: 1.6772\n","output_type":"stream"},{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.history.History at 0x7ebe54693c40>"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\n# Train the model and store the history\nhistory = model_variation_1.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=5, batch_size=32)\n\n# Plot training and validation loss\nplt.plot(history.history['loss'], label='Training Loss')\nplt.plot(history.history['val_loss'], label='Validation Loss')\nplt.xlabel('Epoch')\nplt.ylabel('Loss')\nplt.legend()\nplt.title('Training and Validation Loss')\nplt.show()\n\n# Plot training and validation accuracy\nplt.plot(history.history['accuracy'], label='Training Accuracy')\nplt.plot(history.history['val_accuracy'], label='Validation Accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.legend()\nplt.title('Training and Validation Accuracy')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:10:43.424268Z","iopub.execute_input":"2024-04-01T16:10:43.424973Z","iopub.status.idle":"2024-04-01T16:11:38.641991Z","shell.execute_reply.started":"2024-04-01T16:10:43.424927Z","shell.execute_reply":"2024-04-01T16:11:38.641104Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Epoch 1/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 14ms/step - accuracy: 0.3214 - loss: 1.6910 - val_accuracy: 0.3338 - val_loss: 1.6760\nEpoch 2/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - accuracy: 0.3247 - loss: 1.6775 - val_accuracy: 0.3483 - val_loss: 1.6613\nEpoch 3/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 13ms/step - accuracy: 0.3328 - loss: 1.6656 - val_accuracy: 0.3496 - val_loss: 1.6569\nEpoch 4/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - accuracy: 0.3401 - loss: 1.6575 - val_accuracy: 0.3496 - val_loss: 1.6556\nEpoch 5/5\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - accuracy: 0.3443 - loss: 1.6575 - val_accuracy: 0.3504 - val_loss: 1.6507\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}}]},{"cell_type":"code","source":"import mlflow\nimport mlflow.tensorflow\nfrom tensorflow.keras.applications import ResNet50\nfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.callbacks import History\nimport os\n\n# Set the MLflow experiment\nmlflow.set_experiment(\"/mlflow-tf-keras-resnet\")\n\n# Log parameters\nmlflow.log_param(\"epochs\", 5)\nmlflow.log_param(\"dense_units\", 2048)  # Log the number of units in the Dense layer\n\n# Define ResNet model with pre-trained weights (transfer learning)\nbase_model = ResNet50(weights='imagenet', include_top=False)\nx = base_model.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(2048, activation='relu')(x)  # Increased units in Dense layer\npredictions = Dense(7, activation='softmax')(x)\nmodel_variation_1 = Model(inputs=base_model.input, outputs=predictions)\n\n# Freeze pre-trained layers\nfor layer in base_model.layers:\n    layer.trainable = False\n\n# Compile the model\nmodel_variation_1.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n\n# Train the model and log metrics\nhistory = History()\nmodel_variation_1.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=2, batch_size=32, callbacks=[history])\n\n# Log metrics\nfor epoch, loss in enumerate(history.history['loss'], 1):\n    mlflow.log_metric(\"train_loss_epoch_\" + str(epoch), loss)\nfor epoch, accuracy in enumerate(history.history['accuracy'], 1):\n    mlflow.log_metric(\"train_accuracy_epoch_\" + str(epoch), accuracy)\nfor epoch, val_loss in enumerate(history.history['val_loss'], 1):\n    mlflow.log_metric(\"val_loss_epoch_\" + str(epoch), val_loss)\nfor epoch, val_accuracy in enumerate(history.history['val_accuracy'], 1):\n    mlflow.log_metric(\"val_accuracy_epoch_\" + str(epoch), val_accuracy)\n\n# Save example images to a directory\nexample_images_dir = 'example_images'\nos.makedirs(example_images_dir, exist_ok=True)\nfor i in range(5):  # Save 5 example images\n    image_path = os.path.join(example_images_dir, f\"example_image_{i}.png\")\n    # Assuming X_train contains images in numpy array format\n    image = X_train[i] * 255.0  # Denormalize the image\n    image = image.astype('uint8')  # Convert to uint8\n    # Save the image\n    plt.imsave(image_path, image.squeeze(), cmap='gray')\n\n# Log example images as artifacts\nmlflow.log_artifacts(example_images_dir, artifact_path=\"example_images\")\n\n# End the MLflow run\nmlflow.end_run()\n","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:11:38.643440Z","iopub.execute_input":"2024-04-01T16:11:38.643867Z","iopub.status.idle":"2024-04-01T16:12:18.513681Z","shell.execute_reply.started":"2024-04-01T16:11:38.643834Z","shell.execute_reply":"2024-04-01T16:12:18.512726Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Epoch 1/2\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 20ms/step - accuracy: 0.2352 - loss: 1.9529 - val_accuracy: 0.2779 - val_loss: 1.7611\nEpoch 2/2\n\u001b[1m785/785\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 14ms/step - accuracy: 0.2732 - loss: 1.7625 - val_accuracy: 0.2840 - val_loss: 1.7431\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Uploading artifacts:   0%|          | 0/5 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4f601ff5dea849f5bc8e67727ed7725f"}},"metadata":{}}]},{"cell_type":"code","source":"from tensorflow.keras.optimizers import RMSprop\n\n# Define ResNet model with pre-trained weights (transfer learning)\nbase_model = ResNet50(weights='imagenet', include_top=False)\nx = base_model.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(1024, activation='relu')(x)\npredictions = Dense(7, activation='softmax')(x)\nmodel_variation_2 = Model(inputs=base_model.input, outputs=predictions)\n\n# Freeze pre-trained layers\nfor layer in base_model.layers:\n    layer.trainable = False\n\n# Compile the model with RMSprop optimizer and different loss function\nmodel_variation_2.compile(optimizer=RMSprop(), loss='categorical_crossentropy', metrics=['accuracy'])\n\n# Train the model\nmodel_variation_2.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=15, batch_size=64)\n","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:12:18.514839Z","iopub.execute_input":"2024-04-01T16:12:18.515128Z","iopub.status.idle":"2024-04-01T16:14:18.589894Z","shell.execute_reply.started":"2024-04-01T16:12:18.515101Z","shell.execute_reply":"2024-04-01T16:14:18.588895Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"Epoch 1/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 35ms/step - accuracy: 0.2082 - loss: 2.1435 - val_accuracy: 0.2588 - val_loss: 1.8082\nEpoch 2/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.2596 - loss: 1.7822 - val_accuracy: 0.2770 - val_loss: 1.7748\nEpoch 3/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.2750 - loss: 1.7622 - val_accuracy: 0.2939 - val_loss: 1.7332\nEpoch 4/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 18ms/step - accuracy: 0.2880 - loss: 1.7367 - val_accuracy: 0.3021 - val_loss: 1.7573\nEpoch 5/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.2929 - loss: 1.7308 - val_accuracy: 0.2995 - val_loss: 1.7029\nEpoch 6/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3082 - loss: 1.7055 - val_accuracy: 0.3002 - val_loss: 1.7404\nEpoch 7/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3075 - loss: 1.7109 - val_accuracy: 0.3214 - val_loss: 1.6945\nEpoch 8/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3126 - loss: 1.6995 - val_accuracy: 0.3117 - val_loss: 1.7172\nEpoch 9/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3187 - loss: 1.6891 - val_accuracy: 0.2770 - val_loss: 1.7525\nEpoch 10/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3184 - loss: 1.6862 - val_accuracy: 0.3009 - val_loss: 1.7367\nEpoch 11/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3203 - loss: 1.6830 - val_accuracy: 0.2894 - val_loss: 1.7194\nEpoch 12/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3263 - loss: 1.6773 - val_accuracy: 0.2844 - val_loss: 1.7362\nEpoch 13/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3335 - loss: 1.6718 - val_accuracy: 0.2894 - val_loss: 1.7141\nEpoch 14/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3329 - loss: 1.6578 - val_accuracy: 0.3331 - val_loss: 1.6658\nEpoch 15/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3348 - loss: 1.6674 - val_accuracy: 0.3006 - val_loss: 1.7316\n","output_type":"stream"},{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.history.History at 0x7eba399bb580>"},"metadata":{}}]},{"cell_type":"code","source":"import mlflow\nimport mlflow.tensorflow\nfrom tensorflow.keras.applications import ResNet50\nfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.optimizers import RMSprop\nimport os\n\n# Set the MLflow experiment\nmlflow.set_experiment(\"/mlflow-tf-resnet-transfer-learning\")\n\n# Log parameters\nmlflow.log_param(\"epochs\", 15)\nmlflow.log_param(\"batch_size\", 64)\n\n# Define ResNet model with pre-trained weights (transfer learning)\nbase_model = ResNet50(weights='imagenet', include_top=False)\nx = base_model.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(1024, activation='relu')(x)\npredictions = Dense(7, activation='softmax')(x)\nmodel_variation_2 = Model(inputs=base_model.input, outputs=predictions)\n\n# Freeze pre-trained layers\nfor layer in base_model.layers:\n    layer.trainable = False\n\n# Compile the model with RMSprop optimizer and categorical cross-entropy loss function\nmodel_variation_2.compile(optimizer=RMSprop(), loss='categorical_crossentropy', metrics=['accuracy'])\n\n# Train the model\nhistory = model_variation_2.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=15, batch_size=64)\n\n# Log metrics\nfor metric_name, values in history.history.items():\n    for epoch, value in enumerate(values, 1):\n        mlflow.log_metric(f\"{metric_name}_epoch_{epoch}\", value)\n\n# Save the model\nmlflow.tensorflow.save_model(model_variation_2, \"model\")\n\n# End the MLflow run\nmlflow.end_run()\n","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:14:18.591054Z","iopub.execute_input":"2024-04-01T16:14:18.591359Z","iopub.status.idle":"2024-04-01T16:16:44.439996Z","shell.execute_reply.started":"2024-04-01T16:14:18.591334Z","shell.execute_reply":"2024-04-01T16:16:44.438975Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stdout","text":"Epoch 1/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 34ms/step - accuracy: 0.2150 - loss: 2.1908 - val_accuracy: 0.2675 - val_loss: 1.7981\nEpoch 2/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.2635 - loss: 1.7748 - val_accuracy: 0.2733 - val_loss: 1.7595\nEpoch 3/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 18ms/step - accuracy: 0.2706 - loss: 1.7649 - val_accuracy: 0.2198 - val_loss: 1.8200\nEpoch 4/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 18ms/step - accuracy: 0.2850 - loss: 1.7406 - val_accuracy: 0.3190 - val_loss: 1.7158\nEpoch 5/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 18ms/step - accuracy: 0.2985 - loss: 1.7269 - val_accuracy: 0.2924 - val_loss: 1.8354\nEpoch 6/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 18ms/step - accuracy: 0.2985 - loss: 1.7136 - val_accuracy: 0.2976 - val_loss: 1.7077\nEpoch 7/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3099 - loss: 1.7082 - val_accuracy: 0.3091 - val_loss: 1.6918\nEpoch 8/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3101 - loss: 1.7010 - val_accuracy: 0.3037 - val_loss: 1.6968\nEpoch 9/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3204 - loss: 1.6932 - val_accuracy: 0.2645 - val_loss: 1.7434\nEpoch 10/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3178 - loss: 1.6838 - val_accuracy: 0.3353 - val_loss: 1.6812\nEpoch 11/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3288 - loss: 1.6783 - val_accuracy: 0.3388 - val_loss: 1.6824\nEpoch 12/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3273 - loss: 1.6761 - val_accuracy: 0.2434 - val_loss: 1.8356\nEpoch 13/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3266 - loss: 1.6791 - val_accuracy: 0.3093 - val_loss: 1.8073\nEpoch 14/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3348 - loss: 1.6673 - val_accuracy: 0.3413 - val_loss: 1.6781\nEpoch 15/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - accuracy: 0.3349 - loss: 1.6686 - val_accuracy: 0.3331 - val_loss: 1.6876\n","output_type":"stream"},{"name":"stderr","text":"2024/04/01 16:16:26 WARNING mlflow.tensorflow: You are saving a TensorFlow Core model or Keras model without a signature. Inference with mlflow.pyfunc.spark_udf() will not work unless the model's pyfunc representation accepts pandas DataFrames as inference inputs.\n/opt/conda/lib/python3.10/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.\n  warnings.warn(\"Setuptools is replacing distutils.\")\n","output_type":"stream"}]},{"cell_type":"code","source":"import mlflow\nimport mlflow.tensorflow\nfrom tensorflow.keras.applications import VGG16\nfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.optimizers import RMSprop\nimport matplotlib.pyplot as plt\nimport os\n\n# Set the MLflow experiment\nmlflow.set_experiment(\"/mlflow-tf-vgg16-transfer-learning\")\n\n# Log parameters\nmlflow.log_param(\"epochs\", 15)\nmlflow.log_param(\"batch_size\", 64)\n\n# Define VGG16 model with pre-trained weights (transfer learning)\nbase_model = VGG16(weights='imagenet', include_top=False)\nx = base_model.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(1024, activation='relu')(x)\npredictions = Dense(7, activation='softmax')(x)\nmodel_variation_3 = Model(inputs=base_model.input, outputs=predictions)\n\n# Freeze pre-trained layers\nfor layer in base_model.layers:\n    layer.trainable = False\n\n# Compile the model with RMSprop optimizer and categorical cross-entropy loss function\nmodel_variation_3.compile(optimizer=RMSprop(), loss='categorical_crossentropy', metrics=['accuracy'])\n\n# Train the model and store history\nhistory = model_variation_3.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=15, batch_size=64)\n\n# Plot and save training and validation curves\nplt.plot(history.history['loss'], label='Training Loss')\nplt.plot(history.history['val_loss'], label='Validation Loss')\nplt.xlabel('Epoch')\nplt.ylabel('Loss')\nplt.legend()\nplt.title('Training and Validation Loss')\nplt.savefig('loss_curve.png')\nplt.show()\n\nplt.plot(history.history['accuracy'], label='Training Accuracy')\nplt.plot(history.history['val_accuracy'], label='Validation Accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.legend()\nplt.title('Training and Validation Accuracy')\nplt.savefig('accuracy_curve.png')\nplt.show()\n\n# Log metrics\nfor metric_name, values in history.history.items():\n    for epoch, value in enumerate(values, 1):\n        mlflow.log_metric(f\"{metric_name}_epoch_{epoch}\", value)\n\n# Save the model and log as artifact\nmlflow.tensorflow.save_model(model_variation_3, \"new_model_path\")\n\nmlflow.log_artifact(\"loss_curve.png\", artifact_path=\"metrics\")\nmlflow.log_artifact(\"accuracy_curve.png\", artifact_path=\"metrics\")\n\n# End the MLflow run\nmlflow.end_run()\n","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:19:52.357526Z","iopub.execute_input":"2024-04-01T16:19:52.358292Z","iopub.status.idle":"2024-04-01T16:22:11.785906Z","shell.execute_reply.started":"2024-04-01T16:19:52.358255Z","shell.execute_reply":"2024-04-01T16:22:11.783864Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"Epoch 1/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 25ms/step - accuracy: 0.3223 - loss: 1.7279 - val_accuracy: 0.3777 - val_loss: 1.5943\nEpoch 2/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.4036 - loss: 1.5414 - val_accuracy: 0.3974 - val_loss: 1.5452\nEpoch 3/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.4335 - loss: 1.4860 - val_accuracy: 0.3899 - val_loss: 1.5571\nEpoch 4/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.4472 - loss: 1.4302 - val_accuracy: 0.4180 - val_loss: 1.5157\nEpoch 5/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.4678 - loss: 1.3898 - val_accuracy: 0.4057 - val_loss: 1.5434\nEpoch 6/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 21ms/step - accuracy: 0.4804 - loss: 1.3456 - val_accuracy: 0.4100 - val_loss: 1.5346\nEpoch 7/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 21ms/step - accuracy: 0.5126 - loss: 1.2915 - val_accuracy: 0.4289 - val_loss: 1.5161\nEpoch 8/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 21ms/step - accuracy: 0.5284 - loss: 1.2440 - val_accuracy: 0.4321 - val_loss: 1.5383\nEpoch 9/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 21ms/step - accuracy: 0.5528 - loss: 1.1941 - val_accuracy: 0.4419 - val_loss: 1.5295\nEpoch 10/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 21ms/step - accuracy: 0.5752 - loss: 1.1390 - val_accuracy: 0.4520 - val_loss: 1.5093\nEpoch 11/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 21ms/step - accuracy: 0.5908 - loss: 1.0976 - val_accuracy: 0.4393 - val_loss: 1.5510\nEpoch 12/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 22ms/step - accuracy: 0.6144 - loss: 1.0485 - val_accuracy: 0.4577 - val_loss: 1.5236\nEpoch 13/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 22ms/step - accuracy: 0.6400 - loss: 0.9908 - val_accuracy: 0.4444 - val_loss: 1.5605\nEpoch 14/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 22ms/step - accuracy: 0.6588 - loss: 0.9459 - val_accuracy: 0.4154 - val_loss: 1.6625\nEpoch 15/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 22ms/step - accuracy: 0.6753 - loss: 0.9080 - val_accuracy: 0.4624 - val_loss: 1.5942\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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                                Traceback (most recent call last)","Cell \u001b[0;32mIn[17], line 60\u001b[0m\n\u001b[1;32m     57\u001b[0m         mlflow\u001b[38;5;241m.\u001b[39mlog_metric(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmetric_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_epoch_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mepoch\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, value)\n\u001b[1;32m     59\u001b[0m \u001b[38;5;66;03m# Save the model and log as artifact\u001b[39;00m\n\u001b[0;32m---> 60\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mtensorflow\u001b[38;5;241m.\u001b[39msave_model(\u001b[43mmodel_variation_8\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnew_model_path\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     62\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_artifact(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mloss_curve.png\u001b[39m\u001b[38;5;124m\"\u001b[39m, artifact_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetrics\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     63\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_artifact(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maccuracy_curve.png\u001b[39m\u001b[38;5;124m\"\u001b[39m, artifact_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetrics\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","\u001b[0;31mNameError\u001b[0m: name 'model_variation_8' is not defined"],"ename":"NameError","evalue":"name 'model_variation_8' is not defined","output_type":"error"}]},{"cell_type":"code","source":"import mlflow\nimport mlflow.tensorflow\nfrom tensorflow.keras.applications import VGG16\nfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.optimizers import RMSprop\nimport matplotlib.pyplot as plt\nimport os\n\n# Set the MLflow experiment\nmlflow.set_experiment(\"/mlflow-tf-vgg16-transfer-learning\")\n\n# Log parameters\nmlflow.log_param(\"epochs\", 15)\nmlflow.log_param(\"batch_size\", 64)\n\n# Define VGG16 model with pre-trained weights (transfer learning)\nbase_model = VGG16(weights='imagenet', include_top=False)\nx = base_model.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(1024, activation='relu')(x)\nx = Dense(512, activation='relu')(x)  # Additional hidden layer\nx = Dense(256, activation='relu')(x)  # Additional hidden layer\nx = Dense(128, activation='relu')(x)  # Additional hidden layer\nx = Dense(64, activation='relu')(x)   # Additional hidden layer\npredictions = Dense(7, activation='softmax')(x)\nvgg_162 = Model(inputs=base_model.input, outputs=predictions)\n\n# Freeze pre-trained layers\nfor layer in base_model.layers:\n    layer.trainable = False\n\n# Compile the model with RMSprop optimizer and categorical cross-entropy loss function\nvgg_162.compile(optimizer=RMSprop(), loss='categorical_crossentropy', metrics=['accuracy'])\n\n# Train the model and store history\nhistory = vgg_162.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=15, batch_size=64)\n\n# Plot and save training and validation curves\nplt.plot(history.history['loss'], label='Training Loss')\nplt.plot(history.history['val_loss'], label='Validation Loss')\nplt.xlabel('Epoch')\nplt.ylabel('Loss')\nplt.legend()\nplt.title('Training and Validation Loss')\nplt.savefig('loss_curve.png')\nplt.show()\n\nplt.plot(history.history['accuracy'], label='Training Accuracy')\nplt.plot(history.history['val_accuracy'], label='Validation Accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.legend()\nplt.title('Training and Validation Accuracy')\nplt.savefig('accuracy_curve.png')\nplt.show()\n\n# Log metrics\nfor metric_name, values in history.history.items():\n    for epoch, value in enumerate(values, 1):\n        mlflow.log_metric(f\"{metric_name}_epoch_{epoch}\", value)\n\n# Save the model and log as artifact\nmlflow.tensorflow.save_model(vgg_162, \"vgg_162\")\nmlflow.log_artifact(\"loss_curve.png\", artifact_path=\"metrics\")\nmlflow.log_artifact(\"accuracy_curve.png\", artifact_path=\"metrics\")\n\n# End the MLflow run\nmlflow.end_run()\n","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:22:51.621298Z","iopub.execute_input":"2024-04-01T16:22:51.621685Z","iopub.status.idle":"2024-04-01T16:25:24.486301Z","shell.execute_reply.started":"2024-04-01T16:22:51.621652Z","shell.execute_reply":"2024-04-01T16:25:24.485222Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"Epoch 1/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 29ms/step - accuracy: 0.2990 - loss: 1.7337 - val_accuracy: 0.3658 - val_loss: 1.6125\nEpoch 2/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.3875 - loss: 1.5767 - val_accuracy: 0.3855 - val_loss: 1.5704\nEpoch 3/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 23ms/step - accuracy: 0.4131 - loss: 1.5164 - val_accuracy: 0.4078 - val_loss: 1.5458\nEpoch 4/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.4328 - loss: 1.4709 - val_accuracy: 0.4111 - val_loss: 1.5352\nEpoch 5/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.4574 - loss: 1.4144 - val_accuracy: 0.4208 - val_loss: 1.5156\nEpoch 6/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.4768 - loss: 1.3581 - val_accuracy: 0.4224 - val_loss: 1.5223\nEpoch 7/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.4961 - loss: 1.3081 - val_accuracy: 0.3977 - val_loss: 1.6146\nEpoch 8/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 22ms/step - accuracy: 0.5264 - loss: 1.2506 - val_accuracy: 0.4507 - val_loss: 1.5040\nEpoch 9/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 22ms/step - accuracy: 0.5533 - loss: 1.1779 - val_accuracy: 0.4401 - val_loss: 1.7272\nEpoch 10/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.5833 - loss: 1.0999 - val_accuracy: 0.4271 - val_loss: 1.6520\nEpoch 11/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.6161 - loss: 1.0117 - val_accuracy: 0.4159 - val_loss: 1.6917\nEpoch 12/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.6401 - loss: 0.9424 - val_accuracy: 0.4574 - val_loss: 1.7148\nEpoch 13/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.6778 - loss: 0.8555 - val_accuracy: 0.4414 - val_loss: 1.8773\nEpoch 14/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.7105 - loss: 0.7894 - val_accuracy: 0.3873 - val_loss: 1.9344\nEpoch 15/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.7321 - loss: 0.7181 - val_accuracy: 0.4444 - val_loss: 1.9699\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}},{"name":"stderr","text":"2024/04/01 16:25:16 WARNING mlflow.tensorflow: You are saving a TensorFlow Core model or Keras model without a signature. Inference with mlflow.pyfunc.spark_udf() will not work unless the model's pyfunc representation accepts pandas DataFrames as inference inputs.\n","output_type":"stream"}]},{"cell_type":"code","source":"import mlflow\nimport mlflow.tensorflow\nfrom tensorflow.keras.applications import VGG16\nfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.optimizers import RMSprop\nimport matplotlib.pyplot as plt\nimport os\nimport time\n\n# Set the MLflow experiment\nmlflow.set_experiment(\"/mlflow-tf-vgg16-transfer-learning\")\n\n# Log parameters\nmlflow.log_param(\"epochs\", 15)\nmlflow.log_param(\"batch_size\", 64)\n\n# Define VGG16 model with pre-trained weights (transfer learning)\nbase_model = VGG16(weights='imagenet', include_top=False)\nx = base_model.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(1024, activation='relu')(x)\npredictions = Dense(7, activation='softmax')(x)\nmodel_variation_3 = Model(inputs=base_model.input, outputs=predictions)\n\n# Freeze pre-trained layers\nfor layer in base_model.layers:\n    layer.trainable = False\n\n# Compile the model with RMSprop optimizer and categorical cross-entropy loss function\nmodel_variation_3.compile(optimizer=RMSprop(), loss='categorical_crossentropy', metrics=['accuracy'])\n\n# Train the model and store history\nhistory = model_variation_3.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=15, batch_size=64)\n\n# Plot and save training and validation curves\nplt.plot(history.history['loss'], label='Training Loss')\nplt.plot(history.history['val_loss'], label='Validation Loss')\nplt.xlabel('Epoch')\nplt.ylabel('Loss')\nplt.legend()\nplt.title('Training and Validation Loss')\nplt.savefig('loss_curve.png')\nplt.show()\n\nplt.plot(history.history['accuracy'], label='Training Accuracy')\nplt.plot(history.history['val_accuracy'], label='Validation Accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.legend()\nplt.title('Training and Validation Accuracy')\nplt.savefig('accuracy_curve.png')\nplt.show()\n\n# Log metrics\nfor metric_name, values in history.history.items():\n    for epoch, value in enumerate(values, 1):\n        mlflow.log_metric(f\"{metric_name}_epoch_{epoch}\", value)\n\n# Save the model with a different name\ntimestamp = int(time.time())\nmlflow.tensorflow.save_model(model_variation_3, f\"model_{timestamp}\")\n\nmlflow.log_artifact(\"loss_curve.png\", artifact_path=\"metrics\")\nmlflow.log_artifact(\"accuracy_curve.png\", artifact_path=\"metrics\")\n\n# End the MLflow run\nmlflow.end_run()\n","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:35:51.895150Z","iopub.execute_input":"2024-04-01T16:35:51.896059Z","iopub.status.idle":"2024-04-01T16:38:20.904820Z","shell.execute_reply.started":"2024-04-01T16:35:51.896025Z","shell.execute_reply":"2024-04-01T16:38:20.904026Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"Epoch 1/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 26ms/step - accuracy: 0.3230 - loss: 1.7203 - val_accuracy: 0.3810 - val_loss: 1.6145\nEpoch 2/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 23ms/step - accuracy: 0.4021 - loss: 1.5446 - val_accuracy: 0.3961 - val_loss: 1.5362\nEpoch 3/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.4281 - loss: 1.4791 - val_accuracy: 0.3974 - val_loss: 1.5520\nEpoch 4/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 22ms/step - accuracy: 0.4513 - loss: 1.4300 - val_accuracy: 0.4146 - val_loss: 1.5242\nEpoch 5/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 21ms/step - accuracy: 0.4705 - loss: 1.3891 - val_accuracy: 0.3914 - val_loss: 1.5668\nEpoch 6/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 21ms/step - accuracy: 0.4923 - loss: 1.3395 - val_accuracy: 0.4393 - val_loss: 1.4846\nEpoch 7/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 21ms/step - accuracy: 0.5157 - loss: 1.2891 - val_accuracy: 0.4328 - val_loss: 1.5057\nEpoch 8/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 21ms/step - accuracy: 0.5352 - loss: 1.2384 - val_accuracy: 0.4445 - val_loss: 1.5096\nEpoch 9/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 21ms/step - accuracy: 0.5548 - loss: 1.1905 - val_accuracy: 0.4423 - val_loss: 1.5295\nEpoch 10/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 21ms/step - accuracy: 0.5729 - loss: 1.1475 - val_accuracy: 0.4354 - val_loss: 1.5580\nEpoch 11/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 22ms/step - accuracy: 0.5993 - loss: 1.0824 - val_accuracy: 0.4544 - val_loss: 1.5284\nEpoch 12/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.6129 - loss: 1.0440 - val_accuracy: 0.4512 - val_loss: 1.5452\nEpoch 13/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.6393 - loss: 0.9902 - val_accuracy: 0.4460 - val_loss: 1.6105\nEpoch 14/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.6592 - loss: 0.9346 - val_accuracy: 0.4483 - val_loss: 1.6023\nEpoch 15/15\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 22ms/step - accuracy: 0.6752 - loss: 0.8892 - val_accuracy: 0.4516 - val_loss: 1.6303\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}},{"name":"stderr","text":"2024/04/01 16:38:12 WARNING mlflow.tensorflow: You are saving a TensorFlow Core model or Keras model without a signature. Inference with mlflow.pyfunc.spark_udf() will not work unless the model's pyfunc representation accepts pandas DataFrames as inference inputs.\n","output_type":"stream"}]},{"cell_type":"code","source":"import mlflow\nimport mlflow.tensorflow\nfrom tensorflow.keras.applications import ResNet50\nfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.optimizers import Adam\nimport matplotlib.pyplot as plt\nimport os\n\n\n\nmlflow.log_param(\"epochs\", 15)\nmlflow.log_param(\"batch_size\", 64)\n\n# Define ResNet50 model with pre-trained weights (transfer learning)\nbase_model = ResNet50(weights='imagenet', include_top=False)\nx = base_model.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(2048, activation='relu')(x)  # Hidden layer 1\nx = Dense(1024, activation='relu')(x)  # Hidden layer 2\nx = Dense(512, activation='relu')(x)   # Hidden layer 3\nx = Dense(256, activation='relu')(x)   # Hidden layer 4\nx = Dense(128, activation='relu')(x)   # Hidden layer 5\npredictions = Dense(7, activation='softmax')(x)\nresnet_5 = Model(inputs=base_model.input, outputs=predictions)\n\n# Freeze pre-trained layers\nfor layer in base_model.layers:\n    layer.trainable = False\n\n# Compile the model with Adam optimizer and categorical cross-entropy loss function\nresnet_5.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])\n\n# Train the model\nhistory = resnet_5.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=25, batch_size=64)\n\n# Plot and save training and validation curves\nplt.plot(history.history['loss'], label='Training Loss')\nplt.plot(history.history['val_loss'], label='Validation Loss')\nplt.xlabel('Epoch')\nplt.ylabel('Loss')\nplt.legend()\nplt.title('Training and Validation Loss')\nplt.savefig('resnet5_loss_curve.png')\nplt.show()\n\nplt.plot(history.history['accuracy'], label='Training Accuracy')\nplt.plot(history.history['val_accuracy'], label='Validation Accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.legend()\nplt.title('Training and Validation Accuracy')\nplt.savefig('resnet5_accuracy_curve.png')\nplt.show()\n\n# Log metrics\nfor metric_name, values in history.history.items():\n    for epoch, value in enumerate(values, 1):\n        mlflow.log_metric(f\"{metric_name}_epoch_{epoch}\", value)\n\n# Save the model and log as artifact\nmlflow.tensorflow.save_model(resnet_5, \"resnet_5\")\nmlflow.log_artifact(\"resnet5_loss_curve.png\", artifact_path=\"metrics\")\nmlflow.log_artifact(\"resnet5_accuracy_curve.png\", artifact_path=\"metrics\")\n\n# End the MLflow run\nmlflow.end_run()\n","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:26:37.929075Z","iopub.execute_input":"2024-04-01T16:26:37.929448Z","iopub.status.idle":"2024-04-01T16:30:31.180920Z","shell.execute_reply.started":"2024-04-01T16:26:37.929418Z","shell.execute_reply":"2024-04-01T16:30:31.180147Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Epoch 1/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 39ms/step - accuracy: 0.2359 - loss: 1.8389 - val_accuracy: 0.2562 - val_loss: 1.8118\nEpoch 2/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.2602 - loss: 1.7863 - val_accuracy: 0.2809 - val_loss: 1.7588\nEpoch 3/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 19ms/step - accuracy: 0.2728 - loss: 1.7633 - val_accuracy: 0.2798 - val_loss: 1.7515\nEpoch 4/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 19ms/step - accuracy: 0.2831 - loss: 1.7368 - val_accuracy: 0.2957 - val_loss: 1.7247\nEpoch 5/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 19ms/step - accuracy: 0.2917 - loss: 1.7288 - val_accuracy: 0.2720 - val_loss: 1.7641\nEpoch 6/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.2955 - loss: 1.7252 - val_accuracy: 0.2772 - val_loss: 1.7692\nEpoch 7/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.2918 - loss: 1.7224 - val_accuracy: 0.3114 - val_loss: 1.6948\nEpoch 8/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3066 - loss: 1.7085 - val_accuracy: 0.2996 - val_loss: 1.7338\nEpoch 9/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3015 - loss: 1.7081 - val_accuracy: 0.3130 - val_loss: 1.6897\nEpoch 10/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 18ms/step - accuracy: 0.3133 - loss: 1.6951 - val_accuracy: 0.3205 - val_loss: 1.6837\nEpoch 11/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3171 - loss: 1.6832 - val_accuracy: 0.3203 - val_loss: 1.6827\nEpoch 12/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3184 - loss: 1.6784 - val_accuracy: 0.3333 - val_loss: 1.6652\nEpoch 13/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3245 - loss: 1.6718 - val_accuracy: 0.3333 - val_loss: 1.6752\nEpoch 14/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3300 - loss: 1.6725 - val_accuracy: 0.3277 - val_loss: 1.6641\nEpoch 15/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3332 - loss: 1.6619 - val_accuracy: 0.3429 - val_loss: 1.6635\nEpoch 16/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3391 - loss: 1.6511 - val_accuracy: 0.3409 - val_loss: 1.6551\nEpoch 17/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3380 - loss: 1.6559 - val_accuracy: 0.3162 - val_loss: 1.6852\nEpoch 18/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3482 - loss: 1.6399 - val_accuracy: 0.3427 - val_loss: 1.6604\nEpoch 19/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3433 - loss: 1.6450 - val_accuracy: 0.3171 - val_loss: 1.7080\nEpoch 20/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3559 - loss: 1.6359 - val_accuracy: 0.3500 - val_loss: 1.6439\nEpoch 21/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3448 - loss: 1.6423 - val_accuracy: 0.3461 - val_loss: 1.6456\nEpoch 22/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3591 - loss: 1.6221 - val_accuracy: 0.3390 - val_loss: 1.6515\nEpoch 23/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3613 - loss: 1.6233 - val_accuracy: 0.3511 - val_loss: 1.6410\nEpoch 24/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.3544 - loss: 1.6227 - val_accuracy: 0.3481 - val_loss: 1.6379\nEpoch 25/25\n\u001b[1m393/393\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 19ms/step - accuracy: 0.3672 - loss: 1.6185 - val_accuracy: 0.3442 - val_loss: 1.6592\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}},{"name":"stderr","text":"2024/04/01 16:30:15 WARNING mlflow.tensorflow: You are saving a TensorFlow Core model or Keras model without a signature. Inference with mlflow.pyfunc.spark_udf() will not work unless the model's pyfunc representation accepts pandas DataFrames as inference inputs.\n","output_type":"stream"}]},{"cell_type":"code","source":"# Assuming you have trained models\nmodel_1_predictions = model.predict(X_val)\nmodel_2_predictions = model_variation_1.predict(X_val)\nmodel_3_predictions = model_variation_2.predict(X_val)\n\n# Create a list containing predictions from all models\nmodels_predictions = [model_1_predictions, model_2_predictions, model_3_predictions]\n","metadata":{"execution":{"iopub.status.busy":"2024-04-01T16:43:21.162517Z","iopub.execute_input":"2024-04-01T16:43:21.162913Z","iopub.status.idle":"2024-04-01T16:43:45.887605Z","shell.execute_reply.started":"2024-04-01T16:43:21.162870Z","shell.execute_reply":"2024-04-01T16:43:45.886756Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"\u001b[1m169/169\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 28ms/step\n\u001b[1m169/169\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 31ms/step\n\u001b[1m169/169\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 26ms/step\n","output_type":"stream"}]}]}