6. DenseNet3D92.95.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[],"dockerImageVersionId":30787,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install medmnist --quiet\n#!pip install --upgrade tensorflow --quiet\n!pip install --upgrade keras_tuner --quiet\n#!pip install --upgrade keras --quiet","metadata":{"editable":false,"execution":{"iopub.status.busy":"2024-10-23T21:46:00.505367Z","iopub.execute_input":"2024-10-23T21:46:00.505675Z","iopub.status.idle":"2024-10-23T21:46:27.612702Z","shell.execute_reply.started":"2024-10-23T21:46:00.505642Z","shell.execute_reply":"2024-10-23T21:46:27.611479Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"from tqdm import tqdm\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.utils.data as data\nimport torchvision.transforms as transforms\n\nimport medmnist\nfrom medmnist import INFO, Evaluator\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os; os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\nimport keras; import keras_tuner; from keras import layers\nkeras.utils.set_random_seed(23)\n\nimport tensorflow as tf\nfrom tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Dense\nfrom tensorflow.keras.layers import AvgPool2D, GlobalAveragePooling2D, MaxPool2D\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import ReLU, concatenate\nimport tensorflow.keras.backend as K","metadata":{"editable":false,"execution":{"iopub.status.busy":"2024-10-23T21:46:27.615037Z","iopub.execute_input":"2024-10-23T21:46:27.615469Z","iopub.status.idle":"2024-10-23T21:46:45.368206Z","shell.execute_reply.started":"2024-10-23T21:46:27.615420Z","shell.execute_reply":"2024-10-23T21:46:45.367403Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import ConfusionMatrixDisplay\nnp.set_printoptions(precision=2)\ndef print_cm(model):\n    y_pred = model.predict(X_test).argmax(axis = 1)\n    y_true = y_test.argmax(axis = 1)\n\n    disp = ConfusionMatrixDisplay.from_predictions(\n          y_true,\n          y_pred,\n          display_labels=labels,\n          cmap=plt.cm.Blues,\n          normalize=\"true\",\n      )\n    disp.ax_.set_title(\"Confusion Matrix\")\n    fig = disp.ax_.get_figure()\n    fig.set_figwidth(15)\n    fig.set_figheight(15)\n\n    disp = ConfusionMatrixDisplay.from_predictions(\n          y_true,\n          y_pred,\n          display_labels=labels,\n          cmap=plt.cm.Reds,\n          normalize=\"pred\",\n      )\n    disp.ax_.set_title(\"Confusion Matrix\")\n    fig = disp.ax_.get_figure()\n    fig.set_figwidth(15)\n    fig.set_figheight(15)","metadata":{"editable":false,"execution":{"iopub.status.busy":"2024-10-23T21:46:45.369660Z","iopub.execute_input":"2024-10-23T21:46:45.370171Z","iopub.status.idle":"2024-10-23T21:46:45.377563Z","shell.execute_reply.started":"2024-10-23T21:46:45.370137Z","shell.execute_reply":"2024-10-23T21:46:45.376511Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"data_flag = 'organmnist3d'\ndownload = True\n\nNUM_EPOCHS = 3\nBATCH_SIZE = 128\nlr = 0.001\n\ninfo = INFO[data_flag]\nDataClass = getattr(medmnist, info['python_class'])\n\n# load the data\ntrain_dataset = DataClass(split='train', download=download, size = 64)\nval_dataset = DataClass(split='val', download=download, size = 64)\ntest_dataset = DataClass(split='test', download=download, size = 64)\n\n\ntrain_loader = data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)\nval_loader = data.DataLoader(dataset=val_dataset, batch_size=2*BATCH_SIZE, shuffle=True)\ntest_loader = data.DataLoader(dataset=test_dataset, batch_size=2*BATCH_SIZE, shuffle=False)","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","editable":false,"execution":{"iopub.status.busy":"2024-10-23T14:30:40.226957Z","iopub.execute_input":"2024-10-23T14:30:40.227240Z","iopub.status.idle":"2024-10-23T14:30:56.780332Z","shell.execute_reply.started":"2024-10-23T14:30:40.227209Z","shell.execute_reply":"2024-10-23T14:30:56.779524Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Downloading https://zenodo.org/records/10519652/files/organmnist3d_64.npz?download=1 to /root/.medmnist/organmnist3d_64.npz\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 361470105/361470105 [00:10<00:00, 33668263.52it/s]\n","output_type":"stream"},{"name":"stdout","text":"Using downloaded and verified file: /root/.medmnist/organmnist3d_64.npz\nUsing downloaded and verified file: /root/.medmnist/organmnist3d_64.npz\n","output_type":"stream"}]},{"cell_type":"code","source":"import numpy as np\n\nx, y = train_dataset[:]\nx = x[0]\nX_train = x.astype(np.float32); y_train = y.astype(np.uint8)\n\nx, y = val_dataset[:]\nx = x[0]\nX_val = x.astype(np.float32); y_val = y.astype(np.uint8)\n\nx, y = test_dataset[:]\nx = x[0]\nX_test = x.astype(np.float32); y_test = y.astype(np.uint8)\n\n\n\nlabels = list(train_dataset.info[\"label\"].values())\nnum_classes = len(labels)\nprint(X_train.shape, y_train.shape, X_test.shape, y_test.shape)","metadata":{"editable":false,"execution":{"iopub.status.busy":"2024-10-23T14:30:56.781457Z","iopub.execute_input":"2024-10-23T14:30:56.781783Z","iopub.status.idle":"2024-10-23T14:30:59.893198Z","shell.execute_reply.started":"2024-10-23T14:30:56.781738Z","shell.execute_reply":"2024-10-23T14:30:59.892184Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"(971, 64, 64, 64) (971, 1) (610, 64, 64, 64) (610, 1)\n","output_type":"stream"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nnrows = 2; ncols = 10\n_, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16*nrows/ncols), constrained_layout=True)\nfor ax, x, y in zip(axs.flat, X_train, y_train):\n  ax.set_axis_off(); image = (x[:,:,0]*255).astype(np.uint8); ax.set_title(labels[y[0]])\n  ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"none\")","metadata":{"editable":false,"execution":{"iopub.status.busy":"2024-10-23T14:30:59.894482Z","iopub.execute_input":"2024-10-23T14:30:59.895712Z","iopub.status.idle":"2024-10-23T14:31:02.009436Z","shell.execute_reply.started":"2024-10-23T14:30:59.895658Z","shell.execute_reply":"2024-10-23T14:31:02.008500Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1600x320 with 20 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"import numpy as np\n\nx, y = train_dataset[:]\nx = x[0]\nX_train = x.astype(np.float32); y_train = y.astype(np.uint8)\n\nx, y = val_dataset[:]\nx = x[0]\nX_val = x.astype(np.float32); y_val = y.astype(np.uint8)\n\nx, y = test_dataset[:]\nx = x[0]\nX_test = x.astype(np.float32); y_test = y.astype(np.uint8)\n\n\n\nlabels = list(train_dataset.info[\"label\"].values())\nnum_classes = len(labels)\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_val = keras.utils.to_categorical(y_val, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\nprint(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\ninput_shape = X_train[0].shape\ninput_shape","metadata":{"editable":false,"execution":{"iopub.status.busy":"2024-10-23T14:31:02.010705Z","iopub.execute_input":"2024-10-23T14:31:02.011103Z","iopub.status.idle":"2024-10-23T14:31:05.175677Z","shell.execute_reply.started":"2024-10-23T14:31:02.011059Z","shell.execute_reply":"2024-10-23T14:31:05.174575Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"(971, 64, 64, 64) (971, 11) (610, 64, 64, 64) (610, 11)\n","output_type":"stream"},{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"(64, 64, 64)"},"metadata":{}}]},{"cell_type":"markdown","source":"# DenseNet","metadata":{"editable":false}},{"cell_type":"code","source":"\"\"\"\nCreating a 3D DenseNet 121\nCode modified from https://towardsdatascience.com/creating-densenet-121-with-tensorflow-edbc08a956d8\n\"\"\"\n\nimport tensorflow as tf\nfrom tensorflow.keras.layers import Input, Conv3D, BatchNormalization, Dense\nfrom tensorflow.keras.layers import AvgPool3D, GlobalAveragePooling3D, MaxPool3D\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import ReLU, concatenate\nimport tensorflow.keras.backend as K\n\ndef densenet3D(input_shape, n_classes, filters=32):\n    \"\"\"\n        Creates a tf/keras 3D Densenet121 model\n        Args:\n            input_shape (tuple): the input shape - width x height x depth x channels\n                In our case (80, 64, 16, 1) for the smaller datasets and (160, 128, 32, 1) for the bigger dataset.\n            n_classes (int): The number of classes to be predicted.\n            filters (int): number of filters, optional\n        Returns:\n            model (tf.keras.models.Model): The 3D Densenet121 model\n    \"\"\"\n    def bn_rl_conv(x, filters, kernel=1, strides=1):\n        x = BatchNormalization()(x)\n        x = ReLU()(x)\n        x = Conv3D(filters, kernel, strides=strides, padding='same')(x)\n        return x\n    \n    def dense_block(x, repetition):\n        for _ in range(repetition):\n            y = bn_rl_conv(x, 4*filters)\n            y = bn_rl_conv(y, filters, 3)\n            x = concatenate([y,x])\n        return x\n        \n    def transition_layer(x):\n        x = bn_rl_conv(x, K.int_shape(x)[-1] //2 )\n        x = AvgPool3D(2, strides = 2, padding = 'same')(x)\n        return x\n    \n    input = Input (input_shape)\n    x = Conv3D(64, 7, strides = 2, padding = 'same')(input)\n    x = MaxPool3D(3, strides = 2, padding = 'same')(x)\n    \n    for repetition in [6,12,24,16]:\n        d = dense_block(x, repetition)\n        x = transition_layer(d)\n\n    x = GlobalAveragePooling3D()(d)\n    output = Dense(n_classes, activation = 'softmax')(x)\n    \n    model = Model(input, output)\n    return model","metadata":{"execution":{"iopub.status.busy":"2024-10-08T08:27:11.670676Z","iopub.execute_input":"2024-10-08T08:27:11.671569Z","iopub.status.idle":"2024-10-08T08:27:11.757209Z","shell.execute_reply.started":"2024-10-08T08:27:11.671529Z","shell.execute_reply":"2024-10-08T08:27:11.756288Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\"\"\"\nCreating a 2D DenseNet 121\nCode modified from https://towardsdatascience.com/creating-densenet-121-with-tensorflow-edbc08a956d8\n\"\"\"\n\nimport tensorflow as tf\nfrom tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Dense\nfrom tensorflow.keras.layers import AvgPool2D, GlobalAveragePooling2D, MaxPool2D\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import ReLU, concatenate\nimport tensorflow.keras.backend as K\n\ndef densenet2D(input_shape, n_classes, filters=32):\n    \"\"\"\n        Creates a tf/keras 2D Densenet121 model\n        Args:\n            input_shape (tuple): the input shape - width x height x depth\n                In our case (80, 64, 16) for the smaller datasets and (160, 128, 32) for the bigger dataset.\n            n_classes (int): The number of classes to be predicted.\n            filters (int): number of filters, optional\n        Returns:\n            model (tf.keras.models.Model): The 2D Densenet121 model\n    \"\"\"\n    def bn_rl_conv(x, filters, kernel=1, strides=1):\n        x = BatchNormalization()(x)\n        x = ReLU()(x)\n        x = Conv2D(filters, kernel, strides=strides, padding = 'same')(x)\n        return x\n    \n    def dense_block(x, repetition):\n        for _ in range(repetition):\n            y = bn_rl_conv(x, 4*filters)\n            y = bn_rl_conv(y, filters, 3)\n            x = concatenate([y,x])\n        return x\n        \n    def transition_layer(x):\n        x = bn_rl_conv(x, K.int_shape(x)[-1] //2 )\n        x = AvgPool2D(2, strides = 2, padding = 'same')(x)\n        return x\n    \n    input = Input (input_shape)\n    x = Conv2D(64, 7, strides = 2, padding = 'same')(input)\n    x = MaxPool2D(3, strides = 2, padding = 'same')(x)\n    \n    for repetition in [6,12,24,16]:\n        d = dense_block(x, repetition)\n        x = transition_layer(d)\n        \n    x = GlobalAveragePooling2D()(d)\n    output = Dense(n_classes, activation = 'softmax')(x)\n    \n    model = Model(input, output)\n    return model","metadata":{"execution":{"iopub.status.busy":"2024-10-08T08:27:41.666764Z","iopub.execute_input":"2024-10-08T08:27:41.667478Z","iopub.status.idle":"2024-10-08T08:27:41.679527Z","shell.execute_reply.started":"2024-10-08T08:27:41.667438Z","shell.execute_reply":"2024-10-08T08:27:41.678497Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 2D","metadata":{"editable":false}},{"cell_type":"code","source":"filters = 32\nclass MyHyperModel():\n    def bn_rl_conv(self,x, filters, kernel=1, strides=1):\n        x = BatchNormalization()(x)\n        x = ReLU()(x)\n        x = Conv2D(filters, kernel, strides=strides, padding = 'same')(x)\n        return x\n    \n    def dense_block(self,x, repetition):\n        for _ in range(repetition):\n            y = self.bn_rl_conv(x, 4*filters)\n            y = self.bn_rl_conv(y, filters, 3)\n            x = concatenate([y,x])\n        return x\n        \n    def transition_layer(self,x):\n        x = self.bn_rl_conv(x, K.int_shape(x)[-1] //2 )\n        x = AvgPool2D(2, strides = 2, padding = 'same')(x)\n        return x\n\n    def build(self):\n        inputs = Input(input_shape)\n        x = keras.layers.Rescaling(1./(255/2) -1)(inputs)\n        \n        x = Conv2D(64, 7, strides = 2, padding = 'same')(x)\n        x = MaxPool2D(3, strides = 2, padding = 'same')(x)\n\n        for repetition in [6,12,24,16]:\n            d = self.dense_block(x, repetition)\n            x = self.transition_layer(d)\n\n        x = GlobalAveragePooling2D()(d)\n        outputs = Dense(num_classes, activation = 'softmax')(x)\n\n        model = Model(inputs, outputs)\n        opt = keras.optimizers.Adam(learning_rate=0.001619)\n        model.compile(loss=\"categorical_crossentropy\", optimizer=opt, metrics=[\"accuracy\"])\n        return model\n    def fit(self, M, x, y, xy_val, **kwargs):\n        factor = 0.313359; patience = 10\n        reduce_cb = keras.callbacks.ReduceLROnPlateau(\n                monitor='val_accuracy', factor=factor, patience=patience, min_delta=1e-4, min_lr=1e-5)\n        early_cb = keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=2*patience, min_delta=1e-5)\n        #kwargs['callbacks'].extend([reduce_cb, early_cb])\n        return M.fit(x, y, batch_size=128, epochs=50, validation_data=xy_val, **kwargs)","metadata":{"editable":false,"execution":{"iopub.status.busy":"2024-10-16T14:23:32.198585Z","iopub.execute_input":"2024-10-16T14:23:32.199022Z","iopub.status.idle":"2024-10-16T14:23:32.215869Z","shell.execute_reply.started":"2024-10-16T14:23:32.198980Z","shell.execute_reply":"2024-10-16T14:23:32.214583Z"},"trusted":true},"execution_count":58,"outputs":[]},{"cell_type":"code","source":"M = MyHyperModel()","metadata":{"editable":false,"execution":{"iopub.status.busy":"2024-10-16T14:23:35.213959Z","iopub.execute_input":"2024-10-16T14:23:35.215062Z","iopub.status.idle":"2024-10-16T14:23:35.219707Z","shell.execute_reply.started":"2024-10-16T14:23:35.215014Z","shell.execute_reply":"2024-10-16T14:23:35.218665Z"},"trusted":true},"execution_count":59,"outputs":[]},{"cell_type":"code","source":"model = M.build()\nM.fit(model,X_train, y_train, (X_val, y_val), verbose=1)","metadata":{"editable":false,"execution":{"iopub.status.busy":"2024-10-16T14:23:37.189474Z","iopub.execute_input":"2024-10-16T14:23:37.190162Z","iopub.status.idle":"2024-10-16T14:29:15.279790Z","shell.execute_reply.started":"2024-10-16T14:23:37.190121Z","shell.execute_reply":"2024-10-16T14:29:15.278707Z"},"trusted":true},"execution_count":60,"outputs":[{"name":"stdout","text":"Epoch 1/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m246s\u001b[0m 13s/step - accuracy: 0.2330 - loss: 3.0584 - val_accuracy: 0.0807 - val_loss: 3198.8718\nEpoch 2/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 209ms/step - accuracy: 0.6276 - loss: 1.8458 - val_accuracy: 0.0807 - val_loss: 25516.7520\nEpoch 3/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 206ms/step - accuracy: 0.6903 - loss: 0.9255 - val_accuracy: 0.0807 - val_loss: 13921.5762\nEpoch 4/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 203ms/step - accuracy: 0.7920 - loss: 0.6093 - val_accuracy: 0.0807 - val_loss: 8690.1533\nEpoch 5/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 199ms/step - accuracy: 0.8598 - loss: 0.4376 - val_accuracy: 0.0807 - val_loss: 4815.2920\nEpoch 6/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 199ms/step - accuracy: 0.8760 - loss: 0.3486 - val_accuracy: 0.0807 - val_loss: 1382.3746\nEpoch 7/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 198ms/step - accuracy: 0.8966 - loss: 0.2897 - val_accuracy: 0.0807 - val_loss: 521.4177\nEpoch 8/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 200ms/step - accuracy: 0.9427 - loss: 0.1702 - val_accuracy: 0.0870 - val_loss: 137.3327\nEpoch 9/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 204ms/step - accuracy: 0.9690 - loss: 0.1116 - val_accuracy: 0.0994 - val_loss: 49.6265\nEpoch 10/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 201ms/step - accuracy: 0.9800 - loss: 0.0792 - val_accuracy: 0.1925 - val_loss: 25.9939\nEpoch 11/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 199ms/step - accuracy: 0.9675 - loss: 0.1115 - val_accuracy: 0.2112 - val_loss: 22.8920\nEpoch 12/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 200ms/step - accuracy: 0.9327 - loss: 0.2074 - val_accuracy: 0.2360 - val_loss: 21.9424\nEpoch 13/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 200ms/step - accuracy: 0.9465 - loss: 0.1373 - val_accuracy: 0.2919 - val_loss: 8.9908\nEpoch 14/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 200ms/step - accuracy: 0.9492 - loss: 0.1609 - val_accuracy: 0.2360 - val_loss: 14.6025\nEpoch 15/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 202ms/step - accuracy: 0.9634 - loss: 0.1106 - val_accuracy: 0.2484 - val_loss: 12.1071\nEpoch 16/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 201ms/step - accuracy: 0.9882 - loss: 0.0537 - val_accuracy: 0.2484 - val_loss: 17.6912\nEpoch 17/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 202ms/step - accuracy: 0.9815 - loss: 0.0516 - val_accuracy: 0.3851 - val_loss: 6.4749\nEpoch 18/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 199ms/step - accuracy: 0.9869 - loss: 0.0526 - val_accuracy: 0.4348 - val_loss: 4.7854\nEpoch 19/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 202ms/step - accuracy: 0.9879 - loss: 0.0327 - val_accuracy: 0.4907 - val_loss: 4.7738\nEpoch 20/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 201ms/step - accuracy: 0.9874 - loss: 0.0421 - val_accuracy: 0.7640 - val_loss: 1.4058\nEpoch 21/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 208ms/step - accuracy: 0.9882 - loss: 0.0341 - val_accuracy: 0.8012 - val_loss: 1.2809\nEpoch 22/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 208ms/step - accuracy: 0.9996 - loss: 0.0082 - val_accuracy: 0.8447 - val_loss: 0.7979\nEpoch 23/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 204ms/step - accuracy: 0.9940 - loss: 0.0209 - val_accuracy: 0.8385 - val_loss: 0.6949\nEpoch 24/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 198ms/step - accuracy: 0.9890 - loss: 0.0664 - val_accuracy: 0.8385 - val_loss: 0.8196\nEpoch 25/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 198ms/step - accuracy: 0.9908 - loss: 0.0341 - val_accuracy: 0.8758 - val_loss: 0.6889\nEpoch 26/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 199ms/step - accuracy: 0.9871 - loss: 0.0432 - val_accuracy: 0.7640 - val_loss: 1.3944\nEpoch 27/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 204ms/step - accuracy: 0.9756 - loss: 0.0795 - val_accuracy: 0.7950 - val_loss: 1.2462\nEpoch 28/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 201ms/step - accuracy: 0.9745 - loss: 0.0625 - val_accuracy: 0.8447 - val_loss: 0.7769\nEpoch 29/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 201ms/step - accuracy: 0.9623 - loss: 0.1234 - val_accuracy: 0.9317 - val_loss: 0.2955\nEpoch 30/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 200ms/step - accuracy: 0.9880 - loss: 0.0466 - val_accuracy: 0.8944 - val_loss: 0.5271\nEpoch 31/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 196ms/step - accuracy: 0.9767 - loss: 0.0680 - val_accuracy: 0.9379 - val_loss: 0.3046\nEpoch 32/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 202ms/step - accuracy: 0.9913 - loss: 0.0309 - val_accuracy: 0.9441 - val_loss: 0.2286\nEpoch 33/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 203ms/step - accuracy: 0.9973 - loss: 0.0130 - val_accuracy: 0.9317 - val_loss: 0.4097\nEpoch 34/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 204ms/step - accuracy: 0.9978 - loss: 0.0141 - val_accuracy: 0.7516 - val_loss: 2.0762\nEpoch 35/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 202ms/step - accuracy: 0.9980 - loss: 0.0090 - val_accuracy: 0.6149 - val_loss: 4.2650\nEpoch 36/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 201ms/step - accuracy: 0.9978 - loss: 0.0064 - val_accuracy: 0.6335 - val_loss: 3.7367\nEpoch 37/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 200ms/step - accuracy: 0.9987 - loss: 0.0036 - val_accuracy: 0.8075 - val_loss: 1.6312\nEpoch 38/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 202ms/step - accuracy: 0.9984 - loss: 0.0033 - val_accuracy: 0.8944 - val_loss: 0.8376\nEpoch 39/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 210ms/step - accuracy: 0.9984 - loss: 0.0059 - val_accuracy: 0.8820 - val_loss: 0.7768\nEpoch 40/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 206ms/step - accuracy: 0.9975 - loss: 0.0053 - val_accuracy: 0.9130 - val_loss: 0.5626\nEpoch 41/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 205ms/step - accuracy: 0.9975 - loss: 0.0030 - val_accuracy: 0.9441 - val_loss: 0.2971\nEpoch 42/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 200ms/step - accuracy: 1.0000 - loss: 4.4806e-04 - val_accuracy: 0.9441 - val_loss: 0.2717\nEpoch 43/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 199ms/step - accuracy: 1.0000 - loss: 3.5966e-04 - val_accuracy: 0.9503 - val_loss: 0.2519\nEpoch 44/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 205ms/step - accuracy: 1.0000 - loss: 2.3563e-04 - val_accuracy: 0.9503 - val_loss: 0.2377\nEpoch 45/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 205ms/step - accuracy: 1.0000 - loss: 1.9411e-04 - val_accuracy: 0.9565 - val_loss: 0.2274\nEpoch 46/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 206ms/step - accuracy: 1.0000 - loss: 1.6941e-04 - val_accuracy: 0.9565 - val_loss: 0.2194\nEpoch 47/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 204ms/step - accuracy: 1.0000 - loss: 1.5206e-04 - val_accuracy: 0.9565 - val_loss: 0.2126\nEpoch 48/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 199ms/step - accuracy: 1.0000 - loss: 1.3886e-04 - val_accuracy: 0.9627 - val_loss: 0.2067\nEpoch 49/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 202ms/step - accuracy: 1.0000 - loss: 1.2829e-04 - val_accuracy: 0.9689 - val_loss: 0.2014\nEpoch 50/50\n\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 203ms/step - accuracy: 1.0000 - loss: 1.1948e-04 - val_accuracy: 0.9689 - val_loss: 0.1967\n","output_type":"stream"},{"execution_count":60,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.history.History at 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= model.evaluate(X_test, y_test, verbose=0)\nprint(f'Loss: {score[0]:.4}\\nPrecisión: {score[1]:.2%}')","metadata":{"editable":false,"execution":{"iopub.status.busy":"2024-10-16T14:29:39.750579Z","iopub.execute_input":"2024-10-16T14:29:39.751005Z","iopub.status.idle":"2024-10-16T14:29:50.951332Z","shell.execute_reply.started":"2024-10-16T14:29:39.750955Z","shell.execute_reply":"2024-10-16T14:29:50.950346Z"},"trusted":true},"execution_count":62,"outputs":[{"name":"stdout","text":"Loss: 0.7289\nPrecisión: 85.57%\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## 2D with data augmentation","metadata":{"editable":false}},{"cell_type":"code","source":"filters = 32\nclass MyHyperModel():\n    def bn_rl_conv(self,x, filters, kernel=1, strides=1):\n        x = BatchNormalization()(x)\n        x = ReLU()(x)\n        x = Conv2D(filters, kernel, strides=strides, padding = 'same')(x)\n        return x\n    \n    def dense_block(self,x, repetition):\n        for _ in range(repetition):\n            y = self.bn_rl_conv(x, 4*filters)\n            y = self.bn_rl_conv(y, filters, 3)\n            x = concatenate([y,x])\n        return x\n        \n    def transition_layer(self,x):\n        x = self.bn_rl_conv(x, K.int_shape(x)[-1] //2 )\n        x = AvgPool2D(2, strides = 2, padding = 'same')(x)\n        return x\n\n    def build(self):\n        inputs = Input(input_shape)\n        factor = 0.2\n        inputs = keras.layers.RandomRotation(factor, fill_mode=\"nearest\")(inputs)\n        inputs = keras.layers.RandomTranslation(factor, factor, fill_mode=\"nearest\")(inputs)\n        inputs = keras.layers.RandomZoom(factor, fill_mode=\"nearest\")(inputs)\n        x = keras.layers.Rescaling(1./(255/2) -1)(inputs)\n        \n        x = Conv2D(64, 7, strides = 2, padding = 'same')(x)\n        x = MaxPool2D(3, strides = 2, padding = 'same')(x)\n\n        for repetition in [6,12,24,16]:\n            d = self.dense_block(x, repetition)\n            x = self.transition_layer(d)\n\n        x = GlobalAveragePooling2D()(d)\n        outputs = Dense(num_classes, activation = 'softmax')(x)\n\n        model = Model(inputs, outputs)\n        opt = keras.optimizers.Adam(learning_rate=0.001619)\n        model.compile(loss=\"categorical_crossentropy\", optimizer=opt, metrics=[\"accuracy\"])\n        return model\n    def fit(self, M, x, y, xy_val, **kwargs):\n        factor = 0.313359; patience = 10\n        reduce_cb = keras.callbacks.ReduceLROnPlateau(\n                monitor='val_accuracy', factor=factor, patience=patience, min_delta=1e-4, min_lr=1e-5)\n        early_cb = keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=2*patience, min_delta=1e-5)\n        #kwargs['callbacks'] = [reduce_cb]\n        return M.fit(x, y, batch_size=128, epochs=70, validation_data=xy_val, **kwargs)","metadata":{"editable":false,"execution":{"iopub.status.busy":"2024-10-16T14:23:07.777257Z","iopub.status.idle":"2024-10-16T14:23:07.777616Z","shell.execute_reply.started":"2024-10-16T14:23:07.777442Z","shell.execute_reply":"2024-10-16T14:23:07.777459Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"M = MyHyperModel()\nmodel = M.build()\nH = M.fit(model,X_train, y_train, (X_val, y_val), verbose=1)\n\nfig, axes = plt.subplots(1, 2, figsize=(9, 3.5))\nfig.tight_layout(); plt.subplots_adjust(wspace=0.3)\nxx = np.arange(1, len(H.history['loss'])+1)\nax = axes[0]; ax.grid(); ax.set_xlabel('epoch'); ax.set_ylabel('loss')\nax.plot(xx, H.history['loss'], color='b', marker='s')\nax.plot(xx, H.history['val_loss'], color='r', marker='s')\nax = axes[1]; ax.grid(); ax.set_xlabel('epoch'); ax.set_ylabel('accuracy')\nax.plot(xx, H.history['accuracy'], color='b', marker='s')\nax.plot(xx, H.history['val_accuracy'], color='r', marker='s');\n\nprint_cm(model)\n\nscore = model.evaluate(X_test, y_test, verbose=0)\nprint(f'Loss: {score[0]:.4}\\nPrecisión: {score[1]:.2%}')","metadata":{"execution":{"iopub.status.busy":"2024-10-14T15:28:49.456211Z","iopub.execute_input":"2024-10-14T15:28:49.457212Z","iopub.status.idle":"2024-10-14T15:34:31.596932Z","shell.execute_reply.started":"2024-10-14T15:28:49.457164Z","shell.execute_reply":"2024-10-14T15:34:31.596014Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 3D ","metadata":{"editable":false}},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras.layers import Input, Conv3D, BatchNormalization, Dense\nfrom tensorflow.keras.layers import AvgPool3D, GlobalAveragePooling3D, MaxPool3D\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import ReLU, concatenate\nimport tensorflow.keras.backend as K","metadata":{"execution":{"iopub.status.busy":"2024-10-23T21:47:01.024186Z","iopub.execute_input":"2024-10-23T21:47:01.024595Z","iopub.status.idle":"2024-10-23T21:47:01.030032Z","shell.execute_reply.started":"2024-10-23T21:47:01.024557Z","shell.execute_reply":"2024-10-23T21:47:01.029000Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"data_flag = 'organmnist3d'\ndownload = True\n\nNUM_EPOCHS = 3\nBATCH_SIZE = 128\nlr = 0.001\n\ninfo = INFO[data_flag]\nDataClass = getattr(medmnist, info['python_class'])\n\n# load the data\ntrain_dataset = DataClass(split='train', download=download, size = 64)\nval_dataset = DataClass(split='val', download=download, size = 64)\ntest_dataset = DataClass(split='test', download=download, size = 64)\n\nimport numpy as np\n\nx, y = train_dataset[:]\nx = x[0]\nX_train = x.astype(np.float32); y_train = y.astype(np.uint8)\n\nx, y = val_dataset[:]\nx = x[0]\nX_val = x.astype(np.float32); y_val = y.astype(np.uint8)\n\nx, y = test_dataset[:]\nx = x[0]\nX_test = x.astype(np.float32); y_test = y.astype(np.uint8)\n\n\n\nlabels = list(train_dataset.info[\"label\"].values())\nnum_classes = len(labels)\nprint(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\n\nimport matplotlib.pyplot as plt\nnrows = 2; ncols = 10\n_, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16*nrows/ncols), constrained_layout=True)\nfor ax, x, y in zip(axs.flat, X_train, y_train):\n    ax.set_axis_off(); image = (x[:,:,0]*255).astype(np.uint8); ax.set_title(labels[y[0]])\n    ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"none\")\n    \n    \nimport numpy as np\n\nx, y = train_dataset[:]\nx = x[0]\nX_train = x.astype(np.float32); y_train = y.astype(np.uint8)\n\nx, y = val_dataset[:]\nx = x[0]\nX_val = x.astype(np.float32); y_val = y.astype(np.uint8)\n\nx, y = test_dataset[:]\nx = x[0]\nX_test = x.astype(np.float32); y_test = y.astype(np.uint8)\n\n\n\nlabels = list(train_dataset.info[\"label\"].values())\nnum_classes = len(labels)\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_val = keras.utils.to_categorical(y_val, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\nprint(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\ninput_shape = X_train[0].shape\ninput_shape","metadata":{"execution":{"iopub.status.busy":"2024-10-23T21:47:03.495264Z","iopub.execute_input":"2024-10-23T21:47:03.496171Z","iopub.status.idle":"2024-10-23T21:47:34.855213Z","shell.execute_reply.started":"2024-10-23T21:47:03.496131Z","shell.execute_reply":"2024-10-23T21:47:34.854227Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"Downloading https://zenodo.org/records/10519652/files/organmnist3d_64.npz?download=1 to /root/.medmnist/organmnist3d_64.npz\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 361470105/361470105 [00:17<00:00, 21048073.78it/s]\n","output_type":"stream"},{"name":"stdout","text":"Using downloaded and verified file: /root/.medmnist/organmnist3d_64.npz\nUsing downloaded and verified file: /root/.medmnist/organmnist3d_64.npz\n(971, 64, 64, 64) (971, 1) (610, 64, 64, 64) (610, 1)\n(971, 64, 64, 64) (971, 11) (610, 64, 64, 64) (610, 11)\n","output_type":"stream"},{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"(64, 64, 64)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1600x320 with 20 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"X, Y = [],[]\nfor x,y in zip(X_train, )","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"filters = 32\nclass MyHyperModel():\n    def bn_rl_conv(self,x, filters, kernel=1, strides=1):\n        x = BatchNormalization()(x)\n        x = ReLU()(x)\n        x = Conv3D(filters, kernel, strides=strides, padding = 'same')(x)\n        return x\n    \n    def dense_block(self,x, repetition):\n        for _ in range(repetition):\n            y = self.bn_rl_conv(x, 4*filters)\n            y = self.bn_rl_conv(y, filters, 3)\n            x = concatenate([y,x])\n        return x\n        \n    def transition_layer(self,x):\n        x = self.bn_rl_conv(x, K.int_shape(x)[-1] //2 )\n        x = AvgPool3D(2, strides = 2, padding = 'same')(x)\n        return x\n\n    def build(self):\n        inputs = Input(input_shape)\n        factor = 0.3\n        inputs = keras.layers.RandomRotation(factor, fill_mode=\"nearest\")(inputs)\n        inputs = keras.layers.RandomTranslation(factor, factor, fill_mode=\"nearest\")(inputs)\n        inputs = keras.layers.RandomZoom(factor, fill_mode=\"nearest\")(inputs)\n        x = keras.layers.Rescaling(1./255)(inputs)\n        x = keras.layers.Reshape((x.shape[1], x.shape[2], x.shape[3], 1))(x)\n        \n        x = Conv3D(64, 7, strides = 2, padding = 'same')(x)\n        x = MaxPool3D(3, strides = 2, padding = 'same')(x)\n\n        for repetition in [6,12,24,16]:\n            d = self.dense_block(x, repetition)\n            x = self.transition_layer(d)\n\n        x = GlobalAveragePooling3D()(d)\n        outputs = Dense(num_classes, activation = 'softmax')(x)\n\n        model = Model(inputs, outputs)\n        opt = keras.optimizers.Adam(learning_rate=0.001619)\n        model.compile(loss=\"categorical_crossentropy\", optimizer=opt, metrics=[\"accuracy\"])\n        return model\n    def fit(self, M, x, y, xy_val, **kwargs):\n        factor = 0.313359; patience = 5\n        reduce_cb = keras.callbacks.ReduceLROnPlateau(\n                monitor='val_accuracy', factor=factor, patience=patience, min_delta=1e-4, min_lr=1e-5)\n        early_cb = keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=2*patience, min_delta=1e-5)\n        checkpoint_cb = keras.callbacks.ModelCheckpoint(\n                \"/kaggle/working/dncheckpoint.keras\",\n                monitor=\"val_loss\",\n                verbose=0,\n                save_best_only=True,\n                save_weights_only=False,\n                mode=\"auto\",\n                save_freq=\"epoch\",\n                initial_value_threshold=None,\n            )\n        kwargs['callbacks'] = [reduce_cb, checkpoint_cb]\n        return M.fit(x, y, batch_size=64, epochs=70, validation_data=xy_val, **kwargs)","metadata":{"execution":{"iopub.status.busy":"2024-10-23T23:01:50.925587Z","iopub.execute_input":"2024-10-23T23:01:50.926600Z","iopub.status.idle":"2024-10-23T23:01:50.943074Z","shell.execute_reply.started":"2024-10-23T23:01:50.926558Z","shell.execute_reply":"2024-10-23T23:01:50.942045Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"M = MyHyperModel()\nmodel = M.build()\nH = M.fit(model,X_train, y_train, (X_val, y_val), verbose=1)\n\nfig, axes = plt.subplots(1, 2, figsize=(9, 3.5))\nfig.tight_layout(); plt.subplots_adjust(wspace=0.3)\nxx = np.arange(1, len(H.history['loss'])+1)\nax = axes[0]; ax.grid(); ax.set_xlabel('epoch'); ax.set_ylabel('loss')\nax.plot(xx, H.history['loss'], color='b', marker='s')\nax.plot(xx, H.history['val_loss'], color='r', marker='s')\nax = axes[1]; ax.grid(); ax.set_xlabel('epoch'); ax.set_ylabel('accuracy')\nax.plot(xx, H.history['accuracy'], color='b', marker='s')\nax.plot(xx, H.history['val_accuracy'], color='r', marker='s');\n\nprint_cm(model)\n\nscore = model.evaluate(X_test, y_test, verbose=0)\nprint(f'Loss: {score[0]:.4}\\nPrecisión: {score[1]:.2%}')","metadata":{"editable":false,"execution":{"iopub.status.busy":"2024-10-23T23:01:56.588257Z","iopub.execute_input":"2024-10-23T23:01:56.589130Z","iopub.status.idle":"2024-10-23T23:37:52.358401Z","shell.execute_reply.started":"2024-10-23T23:01:56.589088Z","shell.execute_reply":"2024-10-23T23:37:52.357440Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Epoch 1/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m362s\u001b[0m 12s/step - accuracy: 0.2077 - loss: 3.4365 - val_accuracy: 0.0807 - val_loss: 20.1334\nEpoch 2/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 975ms/step - accuracy: 0.4383 - loss: 1.6409 - val_accuracy: 0.0807 - val_loss: 58.1860\nEpoch 3/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 991ms/step - accuracy: 0.6233 - loss: 1.1421 - val_accuracy: 0.0807 - val_loss: 184.1613\nEpoch 4/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 0.7001 - loss: 0.9425 - val_accuracy: 0.0807 - val_loss: 16.5963\nEpoch 5/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 0.7824 - loss: 0.6555 - val_accuracy: 0.0994 - val_loss: 3.3594\nEpoch 6/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.8388 - loss: 0.5103 - val_accuracy: 0.0994 - val_loss: 4.9497\nEpoch 7/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.8668 - loss: 0.4000 - val_accuracy: 0.0994 - val_loss: 5.0536\nEpoch 8/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.8956 - loss: 0.3384 - val_accuracy: 0.0994 - val_loss: 5.7763\nEpoch 9/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9072 - loss: 0.2875 - val_accuracy: 0.0994 - val_loss: 4.9648\nEpoch 10/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9092 - loss: 0.2621 - val_accuracy: 0.0870 - val_loss: 5.8648\nEpoch 11/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9442 - loss: 0.1610 - val_accuracy: 0.0994 - val_loss: 4.8889\nEpoch 12/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9535 - loss: 0.1352 - val_accuracy: 0.0994 - val_loss: 6.3779\nEpoch 13/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9524 - loss: 0.1221 - val_accuracy: 0.0994 - val_loss: 7.2688\nEpoch 14/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9628 - loss: 0.1298 - val_accuracy: 0.0994 - val_loss: 7.8840\nEpoch 15/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9638 - loss: 0.1208 - val_accuracy: 0.0870 - val_loss: 5.8754\nEpoch 16/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9683 - loss: 0.0823 - val_accuracy: 0.0994 - val_loss: 7.0689\nEpoch 17/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9753 - loss: 0.0670 - val_accuracy: 0.0994 - val_loss: 6.0830\nEpoch 18/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9758 - loss: 0.0825 - val_accuracy: 0.0870 - val_loss: 6.2606\nEpoch 19/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9841 - loss: 0.0484 - val_accuracy: 0.0994 - val_loss: 9.0027\nEpoch 20/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9858 - loss: 0.0446 - val_accuracy: 0.0994 - val_loss: 8.7418\nEpoch 21/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9693 - loss: 0.1099 - val_accuracy: 0.0870 - val_loss: 6.1786\nEpoch 22/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9456 - loss: 0.1715 - val_accuracy: 0.0994 - val_loss: 8.0260\nEpoch 23/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9542 - loss: 0.1584 - val_accuracy: 0.0994 - val_loss: 9.6881\nEpoch 24/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9703 - loss: 0.1261 - val_accuracy: 0.0994 - val_loss: 9.8703\nEpoch 25/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9790 - loss: 0.0658 - val_accuracy: 0.0994 - val_loss: 12.9912\nEpoch 26/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9529 - loss: 0.2054 - val_accuracy: 0.0870 - val_loss: 9.7792\nEpoch 27/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9244 - loss: 0.3912 - val_accuracy: 0.0870 - val_loss: 9.1299\nEpoch 28/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9563 - loss: 0.1301 - val_accuracy: 0.1180 - val_loss: 11.6008\nEpoch 29/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9811 - loss: 0.0615 - val_accuracy: 0.0994 - val_loss: 10.7920\nEpoch 30/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9821 - loss: 0.0425 - val_accuracy: 0.0994 - val_loss: 13.8163\nEpoch 31/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9932 - loss: 0.0204 - val_accuracy: 0.0994 - val_loss: 14.6557\nEpoch 32/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9895 - loss: 0.0334 - val_accuracy: 0.0994 - val_loss: 13.7852\nEpoch 33/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9973 - loss: 0.0096 - val_accuracy: 0.0994 - val_loss: 14.2732\nEpoch 34/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9886 - loss: 0.0185 - val_accuracy: 0.0932 - val_loss: 13.0316\nEpoch 35/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9904 - loss: 0.0301 - val_accuracy: 0.0994 - val_loss: 14.3135\nEpoch 36/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9867 - loss: 0.0451 - val_accuracy: 0.0994 - val_loss: 13.3407\nEpoch 37/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9989 - loss: 0.0061 - val_accuracy: 0.0994 - val_loss: 12.3335\nEpoch 38/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 0.0015 - val_accuracy: 0.0994 - val_loss: 11.0141\nEpoch 39/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9989 - loss: 0.0040 - val_accuracy: 0.0994 - val_loss: 10.1141\nEpoch 40/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9992 - loss: 0.0082 - val_accuracy: 0.1366 - val_loss: 8.0302\nEpoch 41/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 0.0013 - val_accuracy: 0.2298 - val_loss: 6.0986\nEpoch 42/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 9.7770e-04 - val_accuracy: 0.3727 - val_loss: 4.4786\nEpoch 43/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 7.2103e-04 - val_accuracy: 0.5342 - val_loss: 3.1410\nEpoch 44/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 6.6870e-04 - val_accuracy: 0.6460 - val_loss: 2.2479\nEpoch 45/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 4.9459e-04 - val_accuracy: 0.7516 - val_loss: 1.6348\nEpoch 46/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 4.5867e-04 - val_accuracy: 0.7764 - val_loss: 1.1565\nEpoch 47/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 3.6021e-04 - val_accuracy: 0.8509 - val_loss: 0.8112\nEpoch 48/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 3.2377e-04 - val_accuracy: 0.8696 - val_loss: 0.6000\nEpoch 49/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 2.7901e-04 - val_accuracy: 0.9193 - val_loss: 0.4641\nEpoch 50/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 2.5008e-04 - val_accuracy: 0.9317 - val_loss: 0.3757\nEpoch 51/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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0.1689\nEpoch 61/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 1.1227e-04 - val_accuracy: 0.9814 - val_loss: 0.1666\nEpoch 62/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 1.0672e-04 - val_accuracy: 0.9814 - val_loss: 0.1649\nEpoch 63/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 1.0177e-04 - val_accuracy: 0.9814 - val_loss: 0.1634\nEpoch 64/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 9.7184e-05 - val_accuracy: 0.9814 - val_loss: 0.1619\nEpoch 65/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 9.3005e-05 - val_accuracy: 0.9814 - val_loss: 0.1608\nEpoch 66/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 8.9123e-05 - val_accuracy: 0.9814 - val_loss: 0.1599\nEpoch 67/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 8.5591e-05 - val_accuracy: 0.9814 - val_loss: 0.1589\nEpoch 68/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 8.2284e-05 - val_accuracy: 0.9814 - val_loss: 0.1583\nEpoch 69/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 7.9235e-05 - val_accuracy: 0.9814 - val_loss: 0.1579\nEpoch 70/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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0.1561\nEpoch 80/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 5.5676e-05 - val_accuracy: 0.9814 - val_loss: 0.1560\nEpoch 81/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 5.4161e-05 - val_accuracy: 0.9814 - val_loss: 0.1559\nEpoch 82/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 5.2720e-05 - val_accuracy: 0.9814 - val_loss: 0.1560\nEpoch 83/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 5.1341e-05 - val_accuracy: 0.9814 - val_loss: 0.1561\nEpoch 84/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 5.0021e-05 - val_accuracy: 0.9814 - val_loss: 0.1562\nEpoch 85/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 4.8759e-05 - val_accuracy: 0.9814 - val_loss: 0.1564\nEpoch 86/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 4.7555e-05 - val_accuracy: 0.9814 - val_loss: 0.1565\nEpoch 87/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 4.6398e-05 - val_accuracy: 0.9814 - val_loss: 0.1565\nEpoch 88/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 4.5282e-05 - val_accuracy: 0.9814 - val_loss: 0.1565\nEpoch 89/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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0.1577\nEpoch 99/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 3.5474e-05 - val_accuracy: 0.9814 - val_loss: 0.1579\nEpoch 100/100\n\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 3.4751e-05 - val_accuracy: 0.9814 - val_loss: 0.1580\n\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 642ms/step\nLoss: 0.4311\nPrecisión: 92.95%\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 900x350 with 2 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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= keras.models.load_model('/kaggle/working/dncheckpoint.keras')\nscore = new_model.evaluate(X_test, y_test, verbose=0)\nprint(f'Loss: {score[0]:.4}\\nPrecisión: {score[1]:.2%}')","metadata":{"execution":{"iopub.status.busy":"2024-10-23T23:38:14.699284Z","iopub.execute_input":"2024-10-23T23:38:14.700116Z","iopub.status.idle":"2024-10-23T23:39:21.838861Z","shell.execute_reply.started":"2024-10-23T23:38:14.700077Z","shell.execute_reply":"2024-10-23T23:39:21.837847Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"Loss: 0.4262\nPrecisión: 92.95%\n","output_type":"stream"}]},{"cell_type":"code","source":"model.save('/kaggle/working/densenet3d.keras') ","metadata":{"execution":{"iopub.status.busy":"2024-10-16T15:36:51.188942Z","iopub.execute_input":"2024-10-16T15:36:51.189875Z","iopub.status.idle":"2024-10-16T15:36:53.210925Z","shell.execute_reply.started":"2024-10-16T15:36:51.189825Z","shell.execute_reply":"2024-10-16T15:36:53.209961Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"markdown","source":"# ViVit","metadata":{"editable":false}},{"cell_type":"markdown","source":"## Zero-shot","metadata":{"editable":false}},{"cell_type":"code","source":"# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy)\n# See https://github.com/keras-team/tf-keras for more details.\nfrom huggingface_hub import from_pretrained_keras\n\nmodel = from_pretrained_keras(\"keras-io/video-vision-transformer\")","metadata":{"execution":{"iopub.status.busy":"2024-10-16T13:49:26.220128Z","iopub.execute_input":"2024-10-16T13:49:26.221016Z","iopub.status.idle":"2024-10-16T13:49:35.336666Z","shell.execute_reply.started":"2024-10-16T13:49:26.220962Z","shell.execute_reply":"2024-10-16T13:49:35.335856Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data_flag = 'organmnist3d'\ndownload = True\n\nNUM_EPOCHS = 3\nBATCH_SIZE = 128\nlr = 0.001\n\ninfo = INFO[data_flag]\nDataClass = getattr(medmnist, info['python_class'])\n\n# load the data\ntrain_dataset = DataClass(split='train', download=download)\nval_dataset = DataClass(split='val', download=download)\ntest_dataset = DataClass(split='test', download=download)\n\nimport numpy as np\n\nx, y = train_dataset[:]\nx = x[0]\nX_train = x.astype(np.float32); y_train = y.astype(np.uint8)\n\nx, y = val_dataset[:]\nx = x[0]\nX_val = x.astype(np.float32); y_val = y.astype(np.uint8)\n\nx, y = test_dataset[:]\nx = x[0]\nX_test = x.astype(np.float32); y_test = y.astype(np.uint8)\n\n\n\nlabels = list(train_dataset.info[\"label\"].values())\nnum_classes = len(labels)\nprint(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\n\nimport matplotlib.pyplot as plt\nnrows = 2; ncols = 10\n_, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16*nrows/ncols), constrained_layout=True)\nfor ax, x, y in zip(axs.flat, X_train, y_train):\n    ax.set_axis_off(); image = (x[:,:,0]*255).astype(np.uint8); ax.set_title(labels[y[0]])\n    ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"none\")\n    \n    \nimport numpy as np\n\nx, y = train_dataset[:]\nx = np.expand_dims(x[0], axis=-1)\nX_train = x.astype(np.float32); y_train = y.astype(np.uint8)\n\nx, y = val_dataset[:]\nx = np.expand_dims(x[0], axis=-1)\nX_val = x.astype(np.float32); y_val = y.astype(np.uint8)\n\nx, y = test_dataset[:]\nx = np.expand_dims(x[0], axis=-1)\nX_test = x.astype(np.float32); y_test = y.astype(np.uint8)\n\n\n\nlabels = list(train_dataset.info[\"label\"].values())\nnum_classes = len(labels)\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_val = keras.utils.to_categorical(y_val, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\nprint(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\ninput_shape = X_train[0].shape\ninput_shape","metadata":{"execution":{"iopub.status.busy":"2024-10-16T13:49:35.350974Z","iopub.execute_input":"2024-10-16T13:49:35.351284Z","iopub.status.idle":"2024-10-16T13:49:41.500233Z","shell.execute_reply.started":"2024-10-16T13:49:35.351252Z","shell.execute_reply":"2024-10-16T13:49:41.499248Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n\nprint_cm(model)\n\nscore = model.evaluate(X_test, y_test, verbose=0)\nprint(f'Loss: {score[0]:.4}\\nPrecisión: {score[1]:.2%}')","metadata":{"execution":{"iopub.status.busy":"2024-10-16T13:50:36.532093Z","iopub.execute_input":"2024-10-16T13:50:36.532501Z","iopub.status.idle":"2024-10-16T13:51:01.343966Z","shell.execute_reply.started":"2024-10-16T13:50:36.532462Z","shell.execute_reply":"2024-10-16T13:51:01.342905Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Fine-tuning","metadata":{"editable":false}},{"cell_type":"code","source":"# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy)\n# See https://github.com/keras-team/tf-keras for more details.\nfrom huggingface_hub import from_pretrained_keras\n\nmodel = from_pretrained_keras(\"keras-io/video-vision-transformer\")","metadata":{"execution":{"iopub.status.busy":"2024-10-16T13:56:40.537511Z","iopub.execute_input":"2024-10-16T13:56:40.538247Z","iopub.status.idle":"2024-10-16T13:56:47.047897Z","shell.execute_reply.started":"2024-10-16T13:56:40.538204Z","shell.execute_reply":"2024-10-16T13:56:47.046954Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data_flag = 'organmnist3d'\ndownload = True\n\nNUM_EPOCHS = 3\nBATCH_SIZE = 128\nlr = 0.001\n\ninfo = INFO[data_flag]\nDataClass = getattr(medmnist, info['python_class'])\n\n# load the data\ntrain_dataset = DataClass(split='train', download=download)\nval_dataset = DataClass(split='val', download=download)\ntest_dataset = DataClass(split='test', download=download)\n\nimport numpy as np\n\nx, y = train_dataset[:]\nx = x[0]\nX_train = x.astype(np.float32); y_train = y.astype(np.uint8)\n\nx, y = val_dataset[:]\nx = x[0]\nX_val = x.astype(np.float32); y_val = y.astype(np.uint8)\n\nx, y = test_dataset[:]\nx = x[0]\nX_test = x.astype(np.float32); y_test = y.astype(np.uint8)\n\n\n\nlabels = list(train_dataset.info[\"label\"].values())\nnum_classes = len(labels)\nprint(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\n\nimport matplotlib.pyplot as plt\nnrows = 2; ncols = 10\n_, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16*nrows/ncols), constrained_layout=True)\nfor ax, x, y in zip(axs.flat, X_train, y_train):\n    ax.set_axis_off(); image = (x[:,:,0]*255).astype(np.uint8); ax.set_title(labels[y[0]])\n    ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"none\")\n    \n    \nimport numpy as np\n\nx, y = train_dataset[:]\nx = np.expand_dims(x[0], axis=-1)\nX_train = x.astype(np.float32); y_train = y.astype(np.uint8)\n\nx, y = val_dataset[:]\nx = np.expand_dims(x[0], axis=-1)\nX_val = x.astype(np.float32); y_val = y.astype(np.uint8)\n\nx, y = test_dataset[:]\nx = np.expand_dims(x[0], axis=-1)\nX_test = x.astype(np.float32); y_test = y.astype(np.uint8)\n\n\n\nlabels = list(train_dataset.info[\"label\"].values())\nnum_classes = len(labels)\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_val = keras.utils.to_categorical(y_val, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\nprint(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\ninput_shape = X_train[0].shape\ninput_shape\n","metadata":{"execution":{"iopub.status.busy":"2024-10-15T16:29:49.146041Z","iopub.execute_input":"2024-10-15T16:29:49.146761Z","iopub.status.idle":"2024-10-15T16:29:55.029299Z","shell.execute_reply.started":"2024-10-15T16:29:49.146719Z","shell.execute_reply":"2024-10-15T16:29:55.028333Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.python.keras.callbacks import ReduceLROnPlateau\n\nfactor = 0.313359; patience = 10\nkwargs = {}\nreduce_cb = ReduceLROnPlateau(\n        monitor='val_accuracy', factor=factor, patience=patience, min_delta=1e-4, min_lr=1e-7)\nearly_cb = keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=2*patience, min_delta=1e-5)\nkwargs['callbacks'] = [reduce_cb]","metadata":{"execution":{"iopub.status.busy":"2024-10-16T13:51:44.047665Z","iopub.execute_input":"2024-10-16T13:51:44.048571Z","iopub.status.idle":"2024-10-16T13:51:44.054538Z","shell.execute_reply.started":"2024-10-16T13:51:44.048529Z","shell.execute_reply":"2024-10-16T13:51:44.053539Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\nH = model.fit(X_train, y_train, batch_size=128, epochs=100, validation_data=(X_val, y_val), **kwargs)","metadata":{"execution":{"iopub.status.busy":"2024-10-16T13:58:09.981547Z","iopub.execute_input":"2024-10-16T13:58:09.982435Z","iopub.status.idle":"2024-10-16T13:59:36.179278Z","shell.execute_reply.started":"2024-10-16T13:58:09.982393Z","shell.execute_reply":"2024-10-16T13:59:36.178470Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 2, figsize=(9, 3.5))\nfig.tight_layout(); plt.subplots_adjust(wspace=0.3)\nxx = np.arange(1, len(H.history['loss'])+1)\nax = axes[0]; ax.grid(); ax.set_xlabel('epoch'); ax.set_ylabel('loss')\nax.plot(xx, H.history['loss'], color='b', marker='s')\nax.plot(xx, H.history['val_loss'], color='r', marker='s')\nax = axes[1]; ax.grid(); ax.set_xlabel('epoch'); ax.set_ylabel('accuracy')\nax.plot(xx, H.history['accuracy'], color='b', marker='s')\nax.plot(xx, H.history['val_accuracy'], color='r', marker='s');","metadata":{"execution":{"iopub.status.busy":"2024-10-14T13:53:24.954514Z","iopub.execute_input":"2024-10-14T13:53:24.954862Z","iopub.status.idle":"2024-10-14T13:53:25.350369Z","shell.execute_reply.started":"2024-10-14T13:53:24.954828Z","shell.execute_reply":"2024-10-14T13:53:25.349387Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print_cm(model)\n\nscore = model.evaluate(X_test, y_test, verbose=0)\nprint(f'Loss: {score[0]:.4}\\nPrecisión: {score[1]:.2%}')","metadata":{"execution":{"iopub.status.busy":"2024-10-14T13:53:25.351423Z","iopub.execute_input":"2024-10-14T13:53:25.351720Z","iopub.status.idle":"2024-10-14T13:53:32.398702Z","shell.execute_reply.started":"2024-10-14T13:53:25.351689Z","shell.execute_reply":"2024-10-14T13:53:32.397756Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data Augmentation","metadata":{"editable":false}},{"cell_type":"code","source":"!pip install volumentations-3D --quiet\nfrom volumentations import *\n\ndef get_augmentation():\n    return Compose([Rotate((-15, 15), (0, 0), (0, 0), p=0.5),\n                    ElasticTransform((0, 0.25), interpolation=2, p=0.1),\n                    RandomRotate90((1, 2), p=0.5)], p=1.0)","metadata":{"execution":{"iopub.status.busy":"2024-10-15T16:30:01.122211Z","iopub.execute_input":"2024-10-15T16:30:01.123061Z","iopub.status.idle":"2024-10-15T16:30:12.579551Z","shell.execute_reply.started":"2024-10-15T16:30:01.123020Z","shell.execute_reply":"2024-10-15T16:30:12.578373Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def augmentor(img):\n    aug = get_augmentation()\n    data = {'image': img}\n    aug_data = aug(**data)\n    img     = aug_data['image']\n    return np.ndarray.astype(img , np.float32)","metadata":{"execution":{"iopub.status.busy":"2024-10-15T16:30:12.581798Z","iopub.execute_input":"2024-10-15T16:30:12.582146Z","iopub.status.idle":"2024-10-15T16:30:12.587903Z","shell.execute_reply.started":"2024-10-15T16:30:12.582110Z","shell.execute_reply":"2024-10-15T16:30:12.587034Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nnrows = 1; ncols = 2\n_, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16*nrows/ncols), constrained_layout=True)\nfor ax, x, y in zip(axs.flat, X_train, y_train):\n    ax.set_axis_off(); image = (x[:,:,0]*255).astype(np.uint8); ax.set_title(labels[int(y[0])])\n    ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"none\")","metadata":{"execution":{"iopub.status.busy":"2024-10-15T16:30:12.589071Z","iopub.execute_input":"2024-10-15T16:30:12.589341Z","iopub.status.idle":"2024-10-15T16:30:13.084973Z","shell.execute_reply.started":"2024-10-15T16:30:12.589311Z","shell.execute_reply":"2024-10-15T16:30:13.084083Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_aux = np.array([augmentor(X_train[0]), augmentor(X_train[1])])\ny_aux = y_train[:2]\nimport matplotlib.pyplot as plt\nnrows = 1; ncols = 2\n_, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16*nrows/ncols), constrained_layout=True)\nfor ax, x, y in zip(axs.flat, X_aux, y_aux):\n    ax.set_axis_off(); image = (x[:,:,0]*255).astype(np.uint8); ax.set_title(labels[int(y[0])])\n    ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"none\")","metadata":{"execution":{"iopub.status.busy":"2024-10-15T16:30:13.087415Z","iopub.execute_input":"2024-10-15T16:30:13.088089Z","iopub.status.idle":"2024-10-15T16:30:13.591634Z","shell.execute_reply.started":"2024-10-15T16:30:13.088041Z","shell.execute_reply":"2024-10-15T16:30:13.590670Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\n\ns = pd.Series(y_train.argmax(axis = -1))\nX_augmented = X_train\ny_augmented = y_train\nwhile min(s.value_counts()) != max(s.value_counts()):\n    aux = s[s==s.value_counts()[s.value_counts() == min(s.value_counts())].index[0]]\n    idx = aux.index[random.randint(0, len(aux)-1)]\n    x, y = augmentor(X_augmented[idx]), y_augmented[idx]\n    X_augmented = np.concatenate((X_augmented, np.array([x])), axis = 0)\n    y_augmented = np.concatenate((y_augmented, np.array([y])), axis = 0)\n    s = pd.Series(y_augmented.argmax(axis = -1))\n    print(min(s.value_counts()), end = \"\\r\")","metadata":{"execution":{"iopub.status.busy":"2024-10-15T16:30:13.592760Z","iopub.execute_input":"2024-10-15T16:30:13.593050Z","iopub.status.idle":"2024-10-15T16:30:25.271970Z","shell.execute_reply.started":"2024-10-15T16:30:13.593019Z","shell.execute_reply":"2024-10-15T16:30:25.270957Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for _ in tqdm(range(5)):\n    X_augmented = np.concatenate((X_augmented, X_train), axis = 0)\n    y_augmented = np.concatenate((y_augmented, y_train), axis = 0)\n    s = pd.Series(y_augmented.argmax(axis = -1))\n    while min(s.value_counts()) != max(s.value_counts()):\n        aux = s[s==s.value_counts()[s.value_counts() == min(s.value_counts())].index[0]]\n        idx = aux.index[random.randint(0, len(aux)-1)]\n        x, y = augmentor(X_augmented[idx]), y_augmented[idx]\n        X_augmented = np.concatenate((X_augmented, np.array([x])), axis = 0)\n        y_augmented = np.concatenate((y_augmented, np.array([y])), axis = 0)\n        s = pd.Series(y_augmented.argmax(axis = -1))\n        print(min(s.value_counts()), end = \"\\r\")","metadata":{"execution":{"iopub.status.busy":"2024-10-15T16:30:25.274317Z","iopub.execute_input":"2024-10-15T16:30:25.275057Z","iopub.status.idle":"2024-10-15T16:34:11.649052Z","shell.execute_reply.started":"2024-10-15T16:30:25.275007Z","shell.execute_reply":"2024-10-15T16:34:11.648006Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy)\n# See https://github.com/keras-team/tf-keras for more details.\nfrom huggingface_hub import from_pretrained_keras\n\nmodel = from_pretrained_keras(\"keras-io/video-vision-transformer\")","metadata":{"execution":{"iopub.status.busy":"2024-10-15T16:43:46.025801Z","iopub.execute_input":"2024-10-15T16:43:46.026535Z","iopub.status.idle":"2024-10-15T16:43:51.630364Z","shell.execute_reply.started":"2024-10-15T16:43:46.026494Z","shell.execute_reply":"2024-10-15T16:43:51.629440Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.python.keras.callbacks import ReduceLROnPlateau\n\nfactor = 0.313359; patience = 10\nkwargs = {}\nreduce_cb = ReduceLROnPlateau(\n        monitor='val_accuracy', factor=factor, patience=patience, min_delta=1e-4, min_lr=1e-7)\nearly_cb = keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=2*patience, min_delta=1e-5)\nkwargs['callbacks'] = [reduce_cb]","metadata":{"execution":{"iopub.status.busy":"2024-10-15T16:34:22.482075Z","iopub.execute_input":"2024-10-15T16:34:22.482413Z","iopub.status.idle":"2024-10-15T16:34:22.487837Z","shell.execute_reply.started":"2024-10-15T16:34:22.482357Z","shell.execute_reply":"2024-10-15T16:34:22.486962Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\nH = model.fit(X_augmented, y_augmented, batch_size=128, epochs=25, validation_data=(X_val, y_val), **kwargs)","metadata":{"execution":{"iopub.status.busy":"2024-10-15T16:43:52.256010Z","iopub.execute_input":"2024-10-15T16:43:52.256387Z","iopub.status.idle":"2024-10-15T16:45:45.627255Z","shell.execute_reply.started":"2024-10-15T16:43:52.256350Z","shell.execute_reply":"2024-10-15T16:45:45.626318Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 2, figsize=(9, 3.5))\nfig.tight_layout(); plt.subplots_adjust(wspace=0.3)\nxx = np.arange(1, len(H.history['loss'])+1)\nax = axes[0]; ax.grid(); ax.set_xlabel('epoch'); ax.set_ylabel('loss')\nax.plot(xx, H.history['loss'], color='b', marker='s')\nax.plot(xx, H.history['val_loss'], color='r', marker='s')\nax = axes[1]; ax.grid(); ax.set_xlabel('epoch'); ax.set_ylabel('accuracy')\nax.plot(xx, H.history['accuracy'], color='b', marker='s')\nax.plot(xx, H.history['val_accuracy'], color='r', marker='s');\n\n\nprint_cm(model)\n\nscore = model.evaluate(X_test, y_test, verbose=0)\nprint(f'Loss: {score[0]:.4}\\nPrecisión: {score[1]:.2%}')","metadata":{"execution":{"iopub.status.busy":"2024-10-15T16:45:45.629220Z","iopub.execute_input":"2024-10-15T16:45:45.629709Z","iopub.status.idle":"2024-10-15T16:45:53.343163Z","shell.execute_reply.started":"2024-10-15T16:45:45.629664Z","shell.execute_reply":"2024-10-15T16:45:53.342142Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for _ in tqdm(range(5)):\n    X_augmented = np.concatenate((X_augmented, X_train), axis = 0)\n    y_augmented = np.concatenate((y_augmented, y_train), axis = 0)\n    s = pd.Series(y_augmented.argmax(axis = -1))\n    while min(s.value_counts()) != max(s.value_counts()):\n        aux = s[s==s.value_counts()[s.value_counts() == min(s.value_counts())].index[0]]\n        idx = aux.index[random.randint(0, len(aux)-1)]\n        x, y = augmentor(X_augmented[idx]), y_augmented[idx]\n        X_augmented = np.concatenate((X_augmented, np.array([x])), axis = 0)\n        y_augmented = np.concatenate((y_augmented, np.array([y])), axis = 0)\n        s = pd.Series(y_augmented.argmax(axis = -1))\n        print(min(s.value_counts()), end = \"\\r\")","metadata":{"execution":{"iopub.status.busy":"2024-10-15T16:46:41.875515Z","iopub.execute_input":"2024-10-15T16:46:41.876273Z","iopub.status.idle":"2024-10-15T16:55:07.509378Z","shell.execute_reply.started":"2024-10-15T16:46:41.876232Z","shell.execute_reply":"2024-10-15T16:55:07.508449Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy)\n# See https://github.com/keras-team/tf-keras for more details.\nfrom huggingface_hub import from_pretrained_keras\n\nmodel = from_pretrained_keras(\"keras-io/video-vision-transformer\")\n\n\nfrom tensorflow.python.keras.callbacks import ReduceLROnPlateau\n\nfactor = 0.313359; patience = 10\nkwargs = {}\nreduce_cb = ReduceLROnPlateau(\n        monitor='val_accuracy', factor=factor, patience=patience, min_delta=1e-4, min_lr=1e-7)\nearly_cb = keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=2*patience, min_delta=1e-5)\nkwargs['callbacks'] = [reduce_cb]\n\n\nmodel.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\nH = model.fit(X_augmented, y_augmented, batch_size=128, epochs=6, validation_data=(X_val, y_val), **kwargs)\n\n\nfig, axes = plt.subplots(1, 2, figsize=(9, 3.5))\nfig.tight_layout(); plt.subplots_adjust(wspace=0.3)\nxx = np.arange(1, len(H.history['loss'])+1)\nax = axes[0]; ax.grid(); ax.set_xlabel('epoch'); ax.set_ylabel('loss')\nax.plot(xx, H.history['loss'], color='b', marker='s')\nax.plot(xx, H.history['val_loss'], color='r', marker='s')\nax = axes[1]; ax.grid(); ax.set_xlabel('epoch'); ax.set_ylabel('accuracy')\nax.plot(xx, H.history['accuracy'], color='b', marker='s')\nax.plot(xx, H.history['val_accuracy'], color='r', marker='s');\n\n\nprint_cm(model)\n\nscore = model.evaluate(X_test, y_test, verbose=0)\nprint(f'Loss: {score[0]:.4}\\nPrecisión: {score[1]:.2%}')","metadata":{"execution":{"iopub.status.busy":"2024-10-15T17:03:47.866165Z","iopub.execute_input":"2024-10-15T17:03:47.866664Z","iopub.status.idle":"2024-10-15T17:05:15.072965Z","shell.execute_reply.started":"2024-10-15T17:03:47.866623Z","shell.execute_reply":"2024-10-15T17:05:15.072033Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ViT","metadata":{"editable":false}},{"cell_type":"code","source":"data_flag = 'organmnist3d'\ndownload = True\n\nNUM_EPOCHS = 3\nBATCH_SIZE = 128\nlr = 0.001\n\ninfo = INFO[data_flag]\nDataClass = getattr(medmnist, info['python_class'])\n\n# load the data\ntrain_dataset = DataClass(split='train', download=download, size = 64)\nval_dataset = DataClass(split='val', download=download, size = 64)\ntest_dataset = DataClass(split='test', download=download, size = 64)\n\nimport numpy as np\n\nx, y = train_dataset[:]\nx = x[0]\nX_train = x.astype(np.float32); y_train = y.astype(np.uint8)\n\nx, y = val_dataset[:]\nx = x[0]\nX_val = x.astype(np.float32); y_val = y.astype(np.uint8)\n\nx, y = test_dataset[:]\nx = x[0]\nX_test = x.astype(np.float32); y_test = y.astype(np.uint8)\n\n\n\nlabels = list(train_dataset.info[\"label\"].values())\nnum_classes = len(labels)\nprint(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\n\nimport matplotlib.pyplot as plt\nnrows = 2; ncols = 10\n_, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16*nrows/ncols), constrained_layout=True)\nfor ax, x, y in zip(axs.flat, X_train, y_train):\n    ax.set_axis_off(); image = (x[:,:,0]*255).astype(np.uint8); ax.set_title(labels[y[0]])\n    ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"none\")\n    \n    \nimport numpy as np\n\nx, y = train_dataset[:]\nx = np.expand_dims(x[0], axis=-1)\nX_train = x.astype(np.float32); y_train = y.astype(np.uint8)\n\nx, y = val_dataset[:]\nx = np.expand_dims(x[0], axis=-1)\nX_val = x.astype(np.float32); y_val = y.astype(np.uint8)\n\nx, y = test_dataset[:]\nx = np.expand_dims(x[0], axis=-1)\nX_test = x.astype(np.float32); y_test = y.astype(np.uint8)\n\n\n\nlabels = list(train_dataset.info[\"label\"].values())\nnum_classes = len(labels)\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_val = keras.utils.to_categorical(y_val, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\nprint(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\ninput_shape = X_train[0].shape\ninput_shape","metadata":{"editable":false},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch \nfrom torch import nn\nimport math\n\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n#training parameters\nB = 128 #batch size\nbatch_size = B\n\n#image parameters\nC = 64\nH = 64\nW = 64\nx = torch.rand(B, C, H, W)\n\n#model parameters\nD = 64 #hidden size\nP = 8 #patch size\nN = int(H*W/P**2) #number of tokens\nk = 4 #number of attention heads\nDh = int(D/k) #attention head size\np = 0.1 #dropout rate\nmlp_size = D*4\nL = 4 #number of transformer blocks\nn_classes = num_classes #number of classes\n\n# Image Embeddings [Patch, Class, with Possition Embeddings]\n\nclass Embedding(nn.Module):\n    def __init__(self):\n        super(Embedding, self).__init__()\n\n        self.unfold = nn.Unfold(kernel_size = P, stride = P) #function to create patch vectors (x_p^i)\n        self.project = nn.Linear(P**2*C, D) #patch tokens (E)\n        self.cls_token = nn.Parameter(torch.randn((1, 1, D))) #function to create unbatched class token (x_class) as trainable parameter\n        self.pos_embedding = nn.Parameter(torch.randn((1, N+1, D))) #function to create unbatched position embedding (E_pos) as trainable parameter\n        self.dropout = nn.Dropout(p) \n\n        #Why unbatched? Because it allows to set parameters and functions here\n        #Batched will increase parameter size without effectively improving it\n\n    def forward(self, x, verbose = False): \n        #print(\"######\")\n        #print(\"input image:\", x.shape)\n        x = self.unfold(x).transpose(1, 2) #patch vectors (x_p^i)\n        #print(\"x_p^i:\", x.shape)\n        #print(self.project.weight.dtype, x.dtype)\n        x = self.project(x)\n        #print(\"x_p^i*E:\", x.shape) #tokens for patches (x_p^i*E)\n        cls_token = self.cls_token.expand(x.size()[0], -1, -1) #batched class token (x_class)\n        #print(\"x_class:\", cls_token.shape)\n        x = torch.cat((cls_token, x), dim = 1) #concatenate class, final image token embedding\n        #print(\"patch embedding:\", x.shape)\n        pos_embedding = self.pos_embedding.expand(x.size()[0], -1, -1) #batched pos embedding (E_pos)\n        #print(\"E_pos:\", pos_embedding.shape)\n        z0 = x + pos_embedding #adding both embeddings\n        #print(\"z0:\", z0.shape)\n        z0 = self.dropout(z0) #dropout\n        return z0\n\n\n\n# Single Head Attention\n\nclass Single_Head_Attention(nn.Module):\n    def __init__(self):\n        super(Single_Head_Attention, self).__init__()\n\n        self.U_qkv = nn.Linear(D, 3*Dh)\n        self.softmax = nn.Softmax(dim = -1)\n\n    def forward(self, z):\n        #print(\"z:\", z.shape)\n        qkv = self.U_qkv(z)\n        #print(\"qkv:\", qkv.shape)\n        q = qkv[:,:,:Dh]\n        #print(\"q:\", q.shape)\n        k = qkv[:,:,Dh:2*Dh]\n        #print(\"k:\", k.shape)\n        v = qkv[:,:,2*Dh:]\n        #print(\"v:\", v.shape)\n        qkTbysqrtDh = torch.matmul(q, k.transpose(-2, -1))/math.sqrt(Dh)\n        #print(\"qkTbysqrtDh:\", qkTbysqrtDh.shape)\n        A = self.softmax(qkTbysqrtDh)\n        #print(\"A:\", A.shape)\n        SAz = torch.matmul(A, v)\n        #print(\"SAz:\", SAz)\n\n        return SAz\n    \n# Multi Head Self Attention\n\nclass Multi_Head_Self_Attention(nn.Module):\n    def __init__(self):\n        super(Multi_Head_Self_Attention, self).__init__()\n\n        self.heads = nn.ModuleList([Single_Head_Attention() for _ in range(k)])\n        self.U_msa = nn.Linear(D, D)\n        self.dropout = nn.Dropout(p)\n\n    def forward(self, z):\n        ###print(\"#####\")\n        ###print(\"z:\", z.shape)\n        ConSAz = torch.cat([head(z) for head in self.heads], dim = -1)\n        ###print(\"ConSA(z):\", ConSAz.shape)\n        msaz = self.U_msa(z)\n        ###print(\"MSA(z):\", msaz.shape)\n        msaz = self.dropout(msaz)\n\n        return msaz\n    \n\n# MLP\n\nclass MLP(nn.Module):\n    def __init__(self):\n        super(MLP, self).__init__()\n\n        self.U_mlp = nn.Linear(D, mlp_size)\n        self.gelu = nn.GELU()\n        self.U_mlp2 = nn.Linear(mlp_size, D)\n        self.dropout = nn.Dropout(p)\n\n    def forward(self, z):\n        ##print(\"###MLP###\")\n        ##print(\"z:\", z.shape)\n        z = self.U_mlp(z)\n        ##print(\"mlp(z):\", z.shape)\n        z = self.gelu(z)\n        z = self.dropout(z)\n        z = self.U_mlp2(z)\n        ##print(\"mlp2(gelu(mlp(z))):\", z.shape)\n        z = self.gelu(z)\n        z = self.dropout(z)\n\n        return z\n    \n\n# Transformer Block\n\nclass Transformer_Block(nn.Module):\n    def __init__(self):\n        super(Transformer_Block, self).__init__()\n\n        self.layernorm_1 = nn.LayerNorm(D)\n        self.msa = Multi_Head_Self_Attention()\n        self.layernorm_2 = nn.LayerNorm(D)\n        self.mlp = MLP()\n\n    def forward(self, z):\n        #print(\"###Transformer Block###\")\n        #print(\"z:\", z.shape)\n        z1 = self.layernorm_1(z)\n        #print(\"layernorm_1:\", z1.shape)\n        z1 = self.msa(z1)\n        #print(\"msa(layernorm_1(z)):\", z1.shape)\n        z2 = z + z1\n        #print(\"z + msa(layernorm_1(z)):\", z2.shape)\n        z3 = self.layernorm_2(z2)\n        #print(\"layernorm_2(z + msa(layernorm_1(z))):\", z3.shape)\n        z3 = self.mlp(z3)\n        #print(\"mlp(layernorm_2(z + msa(layernorm_1(z)))):\", z3.shape)\n        z4 = z2 + z3\n        #print(\"z2 + mlp(layernorm_2(z + msa(layernorm_1(z)))):\", z4.shape)\n\n        return z4\n    \n\n\n# ViT\n\nclass ViT(nn.Module):\n    def __init__(self):\n        super(ViT, self).__init__()\n\n        self.embedding = Embedding()\n        self.transformer_encoder = nn.ModuleList([Transformer_Block() for _ in range(L)])\n        self.layernorm = nn.LayerNorm(D)\n        self.U_mlp = nn.Linear(D, n_classes)\n        self.softmax = nn.Softmax(dim = -1)\n\n    def forward(self, x):\n        x = torch.squeeze(x)\n        x = x.to(torch.float32)\n        #print(x.dtype)\n        #print(\"###ViT###\")\n        #print(\"input image:\", x.shape)\n        z = self.embedding(x)\n        #print(\"z:\", z.shape)\n        for block in self.transformer_encoder:\n            z = block(z)\n        #print(\"z:\", z.shape)\n        z = self.layernorm(z)\n        #print(\"layernorm(z):\", z.shape)\n        z = z[:,0,:]\n        #print(\"z:\", z.shape)\n        z = self.U_mlp(z)\n        #print(\"mlp(layernorm(z)):\", z.shape)\n        z = self.softmax(z)\n        \n\n        return z","metadata":{"execution":{"iopub.status.busy":"2024-10-11T10:17:14.845850Z","iopub.execute_input":"2024-10-11T10:17:14.846380Z","iopub.status.idle":"2024-10-11T10:17:15.145788Z","shell.execute_reply.started":"2024-10-11T10:17:14.846330Z","shell.execute_reply":"2024-10-11T10:17:15.144801Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch.nn.functional as F\n\n\ndef get_batch(split):\n    data = train_loader if split == \"train\" else val_loader\n    x, y = next(iter(data))\n    x, y = x.to(device), y.to(device)\n    return x, y\n\n@torch.no_grad()\ndef estimate_loss():\n    out = {}\n    model.eval() #sets to evaluation phase, with our model it does nothing\n    for split in [\"train\", \"val\"]:\n        losses = torch.zeros(eval_iters)\n        for k in range(eval_iters):\n            x, y = get_batch(split)\n            logits = model(x)\n            targets =  torch.squeeze(F.one_hot(y)).to(torch.float32)\n            loss = F.cross_entropy(logits, targets)\n            losses[k] = loss.item()\n        out[split] = losses.mean()\n    model.train() #sets to training phase, with our model it does nothing\n    return out\n","metadata":{"execution":{"iopub.status.busy":"2024-10-10T12:57:12.835664Z","iopub.execute_input":"2024-10-10T12:57:12.836046Z","iopub.status.idle":"2024-10-10T12:57:12.844521Z","shell.execute_reply.started":"2024-10-10T12:57:12.836012Z","shell.execute_reply":"2024-10-10T12:57:12.843349Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def train_one_epoch(epoch_index, tb_writer):\n    running_loss = 0.\n    last_loss = 0.\n    acc = 0\n\n    # Here, we use enumerate(training_loader) instead of\n    # iter(training_loader) so that we can track the batch\n    # index and do some intra-epoch reporting\n    for i, data in enumerate(train_loader):\n        # Every data instance is an input + label pair\n        inputs, labels = data\n        inputs, labels = inputs.to(device), labels.to(device)\n\n        # Zero your gradients for every batch!\n        optimizer.zero_grad()\n\n        # Make predictions for this batch\n        outputs = model(inputs)\n\n        # Compute the loss and its gradients\n        labels_onehot = torch.squeeze(F.one_hot(labels)).to(torch.float32)\n        #print(outputs.size(), labels.size())\n        #print(outputs.dtype, labels.dtype)\n        \n        loss = F.cross_entropy(outputs, labels_onehot)\n        loss.backward()\n        acc += (torch.argmax(outputs, dim = 1) == labels).sum().item()\n\n        # Adjust learning weights\n        optimizer.step()\n\n        # Gather data and report\n        running_loss += loss.item()\n        if i % 8 == 7:\n            last_loss = running_loss / 8 # loss per batch\n            print('  batch {} loss: {}'.format(i + 1, last_loss))\n            tb_x = epoch_index * len(train_loader) + i + 1\n            tb_writer.add_scalar('Loss/train', last_loss, tb_x)\n            running_loss = 0.\n\n    return last_loss, acc/X_train.shape[0]","metadata":{"execution":{"iopub.status.busy":"2024-10-10T12:59:28.363572Z","iopub.execute_input":"2024-10-10T12:59:28.363973Z","iopub.status.idle":"2024-10-10T12:59:28.373447Z","shell.execute_reply.started":"2024-10-10T12:59:28.363936Z","shell.execute_reply":"2024-10-10T12:59:28.372528Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from datetime import datetime\nfrom torch.utils.tensorboard import SummaryWriter\n\n# Initializing in a separate cell so we can easily add more epochs to the same run\ntimestamp = datetime.now().strftime('%Y%m%d_%H%M%S')\nwriter = SummaryWriter('runs/fashion_trainer_{}'.format(timestamp))\nepoch_number = 0\nm = ViT()\nmodel = m.to(device)\noptimizer = torch.optim.AdamW(m.parameters(), lr = 0.01)\n\nEPOCHS = 10\n\nbest_vloss = 1_000_000.\n\nfor epoch in range(EPOCHS):\n    print('EPOCH {}:'.format(epoch_number + 1))\n\n    # Make sure gradient tracking is on, and do a pass over the data\n    model.train(True)\n    avg_loss, acc = train_one_epoch(epoch_number, writer)\n\n\n    running_vloss = 0.0\n    vacc = 0\n    # Set the model to evaluation mode, disabling dropout and using population\n    # statistics for batch normalization.\n    model.eval()\n\n    # Disable gradient computation and reduce memory consumption.\n    with torch.no_grad():\n        for i, vdata in enumerate(val_loader):\n            vinputs, vlabels = vdata\n            vinputs, vlabels = vinputs.to(device), vlabels.to(device)\n            voutputs = model(vinputs)\n            vlabels_onehot = torch.squeeze(F.one_hot(vlabels)).to(torch.float32)\n            vloss = F.cross_entropy(voutputs, vlabels_onehot)\n            running_vloss += vloss\n            vacc += (torch.argmax(voutputs, dim = 1) == vlabels).sum().item()\n\n    avg_vloss = running_vloss / (i + 1)\n    print('LOSS train {} valid {}, accuracy {}, accuracy val {}'.format(avg_loss, avg_vloss, acc, vacc/X_val.shape[0]))\n\n    # Log the running loss averaged per batch\n    # for both training and validation\n    writer.add_scalars('Training vs. Validation Loss',\n                    { 'Training' : avg_loss, 'Validation' : avg_vloss },\n                    epoch_number + 1)\n    writer.flush()\n\n    # Track best performance, and save the model's state\n    if avg_vloss < best_vloss:\n        best_vloss = avg_vloss\n        model_path = 'model_{}_{}'.format(timestamp, epoch_number)\n        torch.save(model.state_dict(), model_path)\n\n    epoch_number += 1","metadata":{"execution":{"iopub.status.busy":"2024-10-10T13:01:30.209703Z","iopub.execute_input":"2024-10-10T13:01:30.210084Z","iopub.status.idle":"2024-10-10T13:02:11.186246Z","shell.execute_reply.started":"2024-10-10T13:01:30.210049Z","shell.execute_reply":"2024-10-10T13:02:11.185282Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Running on\", device)\nmodel = ViT()\nm = model.to(device)\n#TRAIN THE MODEL\noptimizer = torch.optim.AdamW(m.parameters(), lr = 0.001)\n\nmax_iters = 200\neval_interval = 20\neval_iters = 10\n\nfor it in range(max_iters):\n    if it % eval_interval == 0:\n        losses = estimate_loss()\n        print(f\"step {it}: train_loss {losses['train']:4f}, val_loss {losses['val']:4f}\")\n    xb, yb = get_batch(\"train\")\n\n    logits = m(xb)\n    #targets =  torch.squeeze(F.one_hot(yb)).to(torch.float32)\n    #print(logits.shape, torch.squeeze(yb).shape)\n    loss = F.cross_entropy(logits, torch.squeeze(yb))\n    optimizer.zero_grad(set_to_none = True)\n    loss.backward()\n    optimizer.step()\n\nprint(loss.item())","metadata":{"execution":{"iopub.status.busy":"2024-10-10T13:20:59.275602Z","iopub.execute_input":"2024-10-10T13:20:59.276588Z","iopub.status.idle":"2024-10-10T13:24:04.550306Z","shell.execute_reply.started":"2024-10-10T13:20:59.276512Z","shell.execute_reply":"2024-10-10T13:24:04.549298Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from torch.optim import Adam\nfrom tqdm import tqdm\n#TRAINING LOOP\ndef train(model, train, val, learning_rate, epochs, batch_size=8, loss=nn.CrossEntropyLoss()):\n    train_dataloader = train_loader\n    val_dataloader = val_loader\n    use_cuda = torch.cuda.is_available()\n    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n    optimizer = Adam(model.parameters(), lr= learning_rate)\n    if use_cuda:\n        model = model.cuda()\n        criterion = loss.cuda()\n    for epoch_num in range(epochs):\n        total_acc_train = 0\n        total_loss_train = 0\n        for train_input, train_label in tqdm(train_dataloader):\n            train_label = torch.squeeze(train_label).to(device)\n            train_input = train_input.to(device)\n            output = model(train_input)\n            batch_loss = loss(output, train_label)\n            total_loss_train += batch_loss.item()\n            acc = (output.argmax(dim=1) == train_label).sum().item()\n            total_acc_train += acc\n            model.zero_grad()\n            batch_loss.backward()\n            optimizer.step()\n        #validation on the development set\n        total_acc_val = 0\n        total_loss_val = 0\n        with torch.no_grad():\n            for val_input, val_label in val_dataloader:\n                val_label = torch.squeeze(val_label).to(device)\n                val_input = val_input.to(device)\n                output = model(val_input)\n                batch_loss = loss(output, val_label)\n                total_loss_val += batch_loss.item()\n                acc = (output.argmax(dim=1) == val_label).sum().item()\n                total_acc_val += acc\n            print(f'Epochs: {epoch_num + 1} | Train Loss: {total_loss_train / len(train): .3f} \\ | Train Accuracy: {total_acc_train / len(train): .3f} \\| Val Loss: {total_loss_val / len(val): .3f} \\| Val Accuracy: {total_acc_val / len(val): .3f}')\n","metadata":{"execution":{"iopub.status.busy":"2024-10-11T10:17:48.210800Z","iopub.execute_input":"2024-10-11T10:17:48.211227Z","iopub.status.idle":"2024-10-11T10:17:48.223944Z","shell.execute_reply.started":"2024-10-11T10:17:48.211183Z","shell.execute_reply":"2024-10-11T10:17:48.222876Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import f1_score\ndef evaluate(model, test, batch_size=8, evaltype=True):\n    test_dataloader = test_loader\n    use_cuda = torch.cuda.is_available()\n    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n    if use_cuda:\n        model = model.cuda()\n    total_acc_test = 0\n    predict=[]\n    out = None\n    with torch.no_grad():\n        k=0\n        for test_input, test_label in test_dataloader:\n            test_label = torch.squeeze(test_label).to(device)\n            test_input = test_input.to(device)\n            output = model(test_input)\n            if k == 0:\n                out = output\n            else:\n                out = torch.cat((out, output), 0)\n            k+=1\n            if evaltype:\n                acc = (output.argmax(dim=1) == test_label).sum().item()\n                total_acc_test += acc\n    if evaltype:\n        print(f'Test Accuracy: {total_acc_test / len(out.argmax(dim=1)): .3f}')\n    return out.argmax(dim=1)","metadata":{"execution":{"iopub.status.busy":"2024-10-11T10:17:51.723913Z","iopub.execute_input":"2024-10-11T10:17:51.724652Z","iopub.status.idle":"2024-10-11T10:17:51.733543Z","shell.execute_reply.started":"2024-10-11T10:17:51.724609Z","shell.execute_reply":"2024-10-11T10:17:51.732559Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Hyperparameters\nEPOCHS = 200\nBATCH = 128\nLR = 0.005\nmodel = ViT()\n#Fine-tuning the model\ntrain(model, train_loader, val_loader, LR, EPOCHS, BATCH)\n#Validating the model in the test dataset\npreds = evaluate(model, test_loader, BATCH, True)","metadata":{"execution":{"iopub.status.busy":"2024-10-11T10:17:54.691767Z","iopub.execute_input":"2024-10-11T10:17:54.692142Z","iopub.status.idle":"2024-10-11T10:19:17.036178Z","shell.execute_reply.started":"2024-10-11T10:17:54.692105Z","shell.execute_reply":"2024-10-11T10:19:17.034797Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 2a implementación","metadata":{"editable":false}},{"cell_type":"code","source":"def patchify(images, n_patches):\n    n, c, h, w = images.shape\n\n    assert h == w, \"Patchify method is implemented for square images only\"\n\n    patches = torch.zeros(n, n_patches ** 2, h * w * c // n_patches ** 2)\n    patch_size = h // n_patches\n\n    for idx, image in enumerate(images):\n        for i in range(n_patches):\n            for j in range(n_patches):\n                patch = image[:, i * patch_size: (i + 1) * patch_size, j * patch_size: (j + 1) * patch_size]\n                patches[idx, i * n_patches + j] = patch.flatten()\n    return patches","metadata":{"execution":{"iopub.status.busy":"2024-10-16T13:29:00.427539Z","iopub.execute_input":"2024-10-16T13:29:00.427914Z","iopub.status.idle":"2024-10-16T13:29:00.435160Z","shell.execute_reply.started":"2024-10-16T13:29:00.427877Z","shell.execute_reply":"2024-10-16T13:29:00.434140Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_positional_embeddings(sequence_length, d):\n    result = torch.ones(sequence_length, d)\n    for i in range(sequence_length):\n        for j in range(d):\n            result[i][j] = np.sin(i / (10000 ** (j / d))) if j % 2 == 0 else np.cos(i / (10000 ** ((j - 1) / d)))\n    return result","metadata":{"execution":{"iopub.status.busy":"2024-10-16T13:29:00.436240Z","iopub.execute_input":"2024-10-16T13:29:00.436554Z","iopub.status.idle":"2024-10-16T13:29:00.444663Z","shell.execute_reply.started":"2024-10-16T13:29:00.436514Z","shell.execute_reply":"2024-10-16T13:29:00.443710Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class MyMSA(nn.Module):\n    def __init__(self, d, n_heads=2):\n        super(MyMSA, self).__init__()\n        self.d = d\n        self.n_heads = n_heads\n\n        assert d % n_heads == 0, f\"Can't divide dimension {d} into {n_heads} heads\"\n\n        d_head = int(d / n_heads)\n        self.q_mappings = nn.ModuleList([nn.Linear(d_head, d_head) for _ in range(self.n_heads)])\n        self.k_mappings = nn.ModuleList([nn.Linear(d_head, d_head) for _ in range(self.n_heads)])\n        self.v_mappings = nn.ModuleList([nn.Linear(d_head, d_head) for _ in range(self.n_heads)])\n        self.d_head = d_head\n        self.softmax = nn.Softmax(dim=-1)\n\n    def forward(self, sequences):\n        # Sequences has shape (N, seq_length, token_dim)\n        # We go into shape    (N, seq_length, n_heads, token_dim / n_heads)\n        # And come back to    (N, seq_length, item_dim)  (through concatenation)\n        result = []\n        for sequence in sequences:\n            seq_result = []\n            for head in range(self.n_heads):\n                q_mapping = self.q_mappings[head]\n                k_mapping = self.k_mappings[head]\n                v_mapping = self.v_mappings[head]\n\n                seq = sequence[:, head * self.d_head: (head + 1) * self.d_head]\n                q, k, v = q_mapping(seq), k_mapping(seq), v_mapping(seq)\n\n                attention = self.softmax(q @ k.T / (self.d_head ** 0.5))\n                seq_result.append(attention @ v)\n            result.append(torch.hstack(seq_result))\n        return torch.cat([torch.unsqueeze(r, dim=0) for r in result])","metadata":{"execution":{"iopub.status.busy":"2024-10-16T13:29:01.170538Z","iopub.execute_input":"2024-10-16T13:29:01.171422Z","iopub.status.idle":"2024-10-16T13:29:01.182982Z","shell.execute_reply.started":"2024-10-16T13:29:01.171379Z","shell.execute_reply":"2024-10-16T13:29:01.181925Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class MyViTBlock(nn.Module):\n    def __init__(self, hidden_d, n_heads, mlp_ratio=4):\n        super(MyViTBlock, self).__init__()\n        self.hidden_d = hidden_d\n        self.n_heads = n_heads\n\n        self.norm1 = nn.LayerNorm(hidden_d)\n        self.mhsa = MyMSA(hidden_d, n_heads)\n        self.norm2 = nn.LayerNorm(hidden_d)\n        self.mlp = nn.Sequential(\n            nn.Linear(hidden_d, mlp_ratio * hidden_d),\n            nn.GELU(),\n            nn.Linear(mlp_ratio * hidden_d, hidden_d)\n        )\n\n    def forward(self, x):\n        out = x + self.mhsa(self.norm1(x))\n        out = out + self.mlp(self.norm2(out))\n        return out","metadata":{"execution":{"iopub.status.busy":"2024-10-16T13:29:04.200241Z","iopub.execute_input":"2024-10-16T13:29:04.201038Z","iopub.status.idle":"2024-10-16T13:29:04.208075Z","shell.execute_reply.started":"2024-10-16T13:29:04.200996Z","shell.execute_reply":"2024-10-16T13:29:04.207012Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class MyViT(nn.Module):\n    def __init__(self, chw, n_patches=7, n_blocks=2, hidden_d=8, n_heads=2, out_d=11):\n        # Super constructor\n        super(MyViT, self).__init__()\n        \n        # Attributes\n        self.chw = chw # ( C , H , W )\n        self.n_patches = n_patches\n        self.n_blocks = n_blocks\n        self.n_heads = n_heads\n        self.hidden_d = hidden_d\n        \n        # Input and patches sizes\n        assert chw[1] % n_patches == 0, \"Input shape not entirely divisible by number of patches\"\n        assert chw[2] % n_patches == 0, \"Input shape not entirely divisible by number of patches\"\n        self.patch_size = (chw[1] / n_patches, chw[2] / n_patches)\n\n        # 1) Linear mapper\n        self.input_d = int(chw[0] * self.patch_size[0] * self.patch_size[1])\n        self.linear_mapper = nn.Linear(self.input_d, self.hidden_d)\n        \n        # 2) Learnable classification token\n        self.class_token = nn.Parameter(torch.rand(1, self.hidden_d))\n        \n        # 3) Positional embedding\n        self.register_buffer('positional_embeddings', get_positional_embeddings(n_patches ** 2 + 1, hidden_d), persistent=False)\n        \n        # 4) Transformer encoder blocks\n        self.blocks = nn.ModuleList([MyViTBlock(hidden_d, n_heads) for _ in range(n_blocks)])\n        \n        # 5) Classification MLPk\n        self.mlp = nn.Sequential(\n            nn.Linear(self.hidden_d, out_d),\n            nn.Softmax(dim=-1)\n        )\n\n    def forward(self, images):\n        # Dividing images into patches\n        n, c, h, w = images.shape\n        patches = patchify(images, self.n_patches).to(self.positional_embeddings.device)\n        \n        # Running linear layer tokenization\n        # Map the vector corresponding to each patch to the hidden size dimension\n        tokens = self.linear_mapper(patches)\n        \n        # Adding classification token to the tokens\n        tokens = torch.cat((self.class_token.expand(n, 1, -1), tokens), dim=1)\n        \n        # Adding positional embedding\n        out = tokens + self.positional_embeddings.repeat(n, 1, 1)\n        \n        # Transformer Blocks\n        for block in self.blocks:\n            out = block(out)\n            \n        # Getting the classification token only\n        out = out[:, 0]\n        \n        return self.mlp(out) \n","metadata":{"execution":{"iopub.status.busy":"2024-10-16T13:29:06.131267Z","iopub.execute_input":"2024-10-16T13:29:06.131654Z","iopub.status.idle":"2024-10-16T13:29:06.145762Z","shell.execute_reply.started":"2024-10-16T13:29:06.131616Z","shell.execute_reply":"2024-10-16T13:29:06.144639Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\n\nfrom tqdm import tqdm, trange\n\nimport torch\nimport torch.nn as nn\nfrom torch.optim import Adam\nfrom torch.nn import CrossEntropyLoss\nfrom torch.utils.data import DataLoader\n\nfrom torchvision.transforms import ToTensor\nfrom torchvision.datasets.mnist import MNIST\n\nnp.random.seed(0)\ntorch.manual_seed(0)","metadata":{"execution":{"iopub.status.busy":"2024-10-16T13:29:10.718722Z","iopub.execute_input":"2024-10-16T13:29:10.719605Z","iopub.status.idle":"2024-10-16T13:29:10.734886Z","shell.execute_reply.started":"2024-10-16T13:29:10.719568Z","shell.execute_reply":"2024-10-16T13:29:10.733969Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"BATCH_SIZE = 256\ntrain_loader = data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)\nval_loader = data.DataLoader(dataset=val_dataset, batch_size=2*BATCH_SIZE, shuffle=True)\ntest_loader = data.DataLoader(dataset=test_dataset, batch_size=2*BATCH_SIZE, shuffle=False)","metadata":{"execution":{"iopub.status.busy":"2024-10-16T13:29:13.018838Z","iopub.execute_input":"2024-10-16T13:29:13.019202Z","iopub.status.idle":"2024-10-16T13:29:13.024973Z","shell.execute_reply.started":"2024-10-16T13:29:13.019163Z","shell.execute_reply":"2024-10-16T13:29:13.023948Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nimport torch.optim.lr_scheduler as lr_scheduler\n\nabs\n# Defining model and training options\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(\"Using device: \", device, f\"({torch.cuda.get_device_name(device)})\" if torch.cuda.is_available() else \"\")\nmodel = MyViT((64, 64, 64), n_patches=8, n_blocks=2, hidden_d=8, n_heads=2, out_d=11).to(device)\nN_EPOCHS = 100\nLR = 0.005\n\n# Training loop\noptimizer = Adam(model.parameters(), lr=LR)\n#scheduler = lr_scheduler.LinearLR(optimizer, start_factor=1.0, end_factor=0.1, total_iters=100)\ncriterion = nn.CrossEntropyLoss()\n\nlast_loss = 100000\ncount = 0\nmodel.train()\nfor epoch in trange(N_EPOCHS, desc=\"Training\"):\n    train_loss = 0.0\n    for batch in train_loader:\n        x, y = batch\n        x, y = torch.squeeze(x).to(device), torch.squeeze(y).to(device)\n        y_hat = model(x)\n        loss = criterion(y_hat, y)\n\n        train_loss += loss.detach().cpu().item() / len(train_loader)\n\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n        \n    if last_loss <= train_loss: \n        count += 1\n        if count == 3:\n            count = 0\n            #scheduler.step()\n    last_loss = train_loss\n\n    print(f\"Epoch {epoch + 1}/{N_EPOCHS} loss: {train_loss:.2f}, lr {optimizer.param_groups[0]['lr']}\")\n\n# Test loop\nwith torch.no_grad():\n    correct, total = 0, 0\n    test_loss = 0.0\n    for batch in tqdm(test_loader, desc=\"Testing\"):\n        x, y = batch\n        x, y = torch.squeeze(x).to(device), torch.squeeze(y).to(device)\n        y_hat = model(x)\n        loss = criterion(y_hat, y)\n        test_loss += loss.detach().cpu().item() / len(test_loader)\n\n        correct += torch.sum(torch.argmax(y_hat, dim=1) == y).detach().cpu().item()\n        total += len(x)\n    print(f\"Test loss: {test_loss:.2f}\")\n    print(f\"Test accuracy: {correct / total * 100:.2f}%\")","metadata":{"execution":{"iopub.status.busy":"2024-10-14T13:32:27.182519Z","iopub.execute_input":"2024-10-14T13:32:27.183422Z","iopub.status.idle":"2024-10-14T13:51:17.095544Z","shell.execute_reply.started":"2024-10-14T13:32:27.183380Z","shell.execute_reply":"2024-10-14T13:51:17.094381Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Con AdEMAMix ","metadata":{"editable":false}},{"cell_type":"code","source":"\"\"\"\nAdapted from: https://pytorch.org/docs/1.6.0/_modules/torch/optim/adam.html\n\"\"\"\nimport math\nimport torch\nfrom torch.optim import Optimizer\n\n\ndef linear_warmup_scheduler(step, alpha_end, alpha_start=0, warmup=1):\n    if step < warmup:\n        a = step / float(warmup)\n        return (1.0-a) * alpha_start + a * alpha_end\n    return alpha_end\n\n\ndef linear_hl_warmup_scheduler(step, beta_end, beta_start=0, warmup=1):\n\n    def f(beta, eps=1e-8):\n        return math.log(0.5)/math.log(beta+eps)-1\n\n    def f_inv(t):\n        return math.pow(0.5, 1/(t+1))\n\n    if step < warmup:\n        a = step / float(warmup)\n        return f_inv((1.0-a) * f(beta_start) + a * f(beta_end))\n    return beta_end\n\n\nclass AdEMAMix(Optimizer):\n    r\"\"\"Implements the AdEMAMix algorithm.\n\n    Arguments:\n        params (iterable): iterable of parameters to optimize or dicts defining\n            parameter groups\n        lr (float, optional): learning rate (default: 1e-3)\n        betas (Tuple[float, float, float], optional): coefficients used for computing\n            running averages of gradient and its square (default: (0.9, 0.999, 0.9999)) \n            corresponding to beta_1, beta_2, beta_3 in AdEMAMix\n        alpha (float): AdEMAMix alpha coeficient mixing the slow and fast EMAs (default: 2)\n        beta3_warmup (int, optional): number of warmup steps used to increase beta3 (default: None)\n        alpha_warmup: (int, optional): number of warmup steps used to increase alpha (default: None)\n        eps (float, optional): term added to the denominator to improve\n            numerical stability (default: 1e-8)\n        weight_decay (float, optional): weight decay as in AdamW (default: 0)\n    \"\"\"\n\n    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999, 0.9999), alpha=2.0, \n                 beta3_warmup=None, alpha_warmup=None,  eps=1e-8,\n                 weight_decay=0):\n        if not 0.0 <= lr:\n            raise ValueError(\"Invalid learning rate: {}\".format(lr))\n        if not 0.0 <= eps:\n            raise ValueError(\"Invalid epsilon value: {}\".format(eps))\n        if not 0.0 <= betas[0] < 1.0:\n            raise ValueError(\"Invalid beta parameter at index 0: {}\".format(betas[0]))\n        if not 0.0 <= betas[1] < 1.0:\n            raise ValueError(\"Invalid beta parameter at index 1: {}\".format(betas[1]))\n        if not 0.0 <= betas[2] < 1.0:\n            raise ValueError(\"Invalid beta parameter at index 2: {}\".format(betas[2]))\n        if not 0.0 <= weight_decay:\n            raise ValueError(\"Invalid weight_decay value: {}\".format(weight_decay))\n        if not 0.0 <= alpha:\n            raise ValueError(\"Invalid alpha value: {}\".format(alpha))\n        defaults = dict(lr=lr, betas=betas, eps=eps, alpha=alpha, beta3_warmup=beta3_warmup,\n                        alpha_warmup=alpha_warmup, weight_decay=weight_decay)\n        super(AdEMAMix, self).__init__(params, defaults)\n\n    def __setstate__(self, state):\n        super(AdEMAMix, self).__setstate__(state)\n\n    @torch.no_grad()\n    def step(self, closure=None):\n        \"\"\"Performs a single optimization step.\n\n        Arguments:\n            closure (callable, optional): A closure that reevaluates the model\n                and returns the loss.\n        \"\"\"\n        loss = None\n        if closure is not None:\n            with torch.enable_grad():\n                loss = closure()\n\n        for group in self.param_groups:\n            \n            lr = group[\"lr\"]\n            lmbda = group[\"weight_decay\"]\n            eps = group[\"eps\"]\n            beta1, beta2, beta3_final = group[\"betas\"]\n            beta3_warmup = group[\"beta3_warmup\"]\n            alpha_final = group[\"alpha\"]\n            alpha_warmup = group[\"alpha_warmup\"]\n        \n            for p in group['params']:\n                if p.grad is None:\n                    continue\n                grad = p.grad\n                if grad.is_sparse:\n                    raise RuntimeError('AdEMAMix does not support sparse gradients.')\n\n                state = self.state[p]\n\n                # State initialization\n                if len(state) == 0:\n                    state['step'] = 0\n                    # Exponential moving average of gradient values\n                    if beta1 != 0.0: # save memory in case beta1 is 0.0\n                        state['exp_avg_fast'] = torch.zeros_like(p, memory_format=torch.preserve_format)\n                    else: \n                        state['exp_avg_fast'] = None\n                    state['exp_avg_slow'] = torch.zeros_like(p, memory_format=torch.preserve_format)\n                    # Exponential moving average of squared gradient values\n                    state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)\n\n                exp_avg_fast, exp_avg_slow, exp_avg_sq = state['exp_avg_fast'], state['exp_avg_slow'], state['exp_avg_sq']\n\n                state['step'] += 1\n                bias_correction1 = 1 - beta1 ** state['step']\n                bias_correction2 = 1 - beta2 ** state['step']\n\n                # Compute the effective alpha and beta3 in case warmup is used \n                if alpha_warmup is not None:\n                    alpha = linear_warmup_scheduler(state[\"step\"], alpha_end=alpha_final, alpha_start=0, warmup=alpha_warmup)\n                else:\n                    alpha = alpha_final\n                \n                if beta3_warmup is not None:\n                    beta3 = linear_hl_warmup_scheduler(state[\"step\"], beta_end=beta3_final, beta_start=beta1, warmup=beta3_warmup)\n                else:\n                    beta3 = beta3_final\n\n                # Decay the first and second moment running average coefficient\n                if beta1 != 0.0:\n                    exp_avg_fast.mul_(beta1).add_(grad, alpha=1 - beta1)\n                else:\n                    exp_avg_fast = grad\n                exp_avg_slow.mul_(beta3).add_(grad, alpha=1 - beta3)\n                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\n\n                denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)\n\n                update = (exp_avg_fast.div(bias_correction1) + alpha * exp_avg_slow) / denom\n\n                # decay\n                update.add_(p, alpha=lmbda)\n\n                p.add_(-lr * update)\n\n        return loss","metadata":{"execution":{"iopub.status.busy":"2024-10-16T13:29:18.866422Z","iopub.execute_input":"2024-10-16T13:29:18.866783Z","iopub.status.idle":"2024-10-16T13:29:18.894599Z","shell.execute_reply.started":"2024-10-16T13:29:18.866748Z","shell.execute_reply":"2024-10-16T13:29:18.893580Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nimport torch.optim.lr_scheduler as lr_scheduler\n\nabs\n# Defining model and training options\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(\"Using device: \", device, f\"({torch.cuda.get_device_name(device)})\" if torch.cuda.is_available() else \"\")\nmodel = MyViT((64, 64, 64), n_patches=8, n_blocks=2, hidden_d=8, n_heads=2, out_d=11).to(device)\nN_EPOCHS = 100\nLR = 0.005\n\nimport torch\n\n# AdEMAMix parameters\nlr = 1e-3\nbetas = (0.9, 0.999, 0.9999)\nalpha = 8.0\nbeta3_warmup = alpha_warmup = num_iterations = 256_000\nweight_decay = 0.1\n\n\n# create an AdEMAMix optimizer \noptimizer = AdEMAMix(params=model.parameters(), \n               lr=lr, \n               betas=betas, \n               alpha=alpha, \n               beta3_warmup=beta3_warmup, \n               alpha_warmup=alpha_warmup, \n               weight_decay=weight_decay)\n\n#scheduler = lr_scheduler.LinearLR(optimizer, start_factor=1.0, end_factor=0.1, total_iters=100)\ncriterion = nn.CrossEntropyLoss()\n\nlast_loss = 100000\ncount = 0\nmodel.train()\nfor epoch in trange(N_EPOCHS, desc=\"Training\"):\n    train_loss = 0.0\n    for batch in train_loader:\n        x, y = batch\n        x, y = torch.squeeze(x).to(device), torch.squeeze(y).to(device)\n        y_hat = model(x)\n        loss = criterion(y_hat, y)\n\n        train_loss += loss.detach().cpu().item() / len(train_loader)\n\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n        \n    if last_loss <= train_loss: \n        count += 1\n        if count == 3:\n            count = 0\n            #scheduler.step()\n    last_loss = train_loss\n\n    print(f\"Epoch {epoch + 1}/{N_EPOCHS} loss: {train_loss:.2f}, lr {optimizer.param_groups[0]['lr']}\")\n\n# Test loop\nwith torch.no_grad():\n    correct, total = 0, 0\n    test_loss = 0.0\n    for batch in tqdm(test_loader, desc=\"Testing\"):\n        x, y = batch\n        x, y = torch.squeeze(x).to(device), torch.squeeze(y).to(device)\n        y_hat = model(x)\n        loss = criterion(y_hat, y)\n        test_loss += loss.detach().cpu().item() / len(test_loader)\n\n        correct += torch.sum(torch.argmax(y_hat, dim=1) == y).detach().cpu().item()\n        total += len(x)\n    print(f\"Test loss: {test_loss:.2f}\")\n    print(f\"Test accuracy: {correct / total * 100:.2f}%\")","metadata":{"execution":{"iopub.status.busy":"2024-10-16T13:29:32.729320Z","iopub.execute_input":"2024-10-16T13:29:32.729670Z","iopub.status.idle":"2024-10-16T13:49:10.897096Z","shell.execute_reply.started":"2024-10-16T13:29:32.729638Z","shell.execute_reply":"2024-10-16T13:49:10.896096Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[]}]}