smote_interp_attention_Staianet.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:32.966503Z","iopub.status.busy":"2022-12-15T12:05:32.965846Z","iopub.status.idle":"2022-12-15T12:05:39.227349Z","shell.execute_reply":"2022-12-15T12:05:39.226239Z","shell.execute_reply.started":"2022-12-15T12:05:32.966412Z"},"id":"va-QmMMoqrL7","outputId":"3d4afdb2-6264-4e39-cf55-cd995a40df30","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["2.6.4\n"]}],"source":["import os \n","import numpy as np \n","import pandas as pd \n","import seaborn as sns \n","import matplotlib.pyplot as plt\n","import tensorflow as tf \n","import tensorflow.keras as tfk \n","import tensorflow.keras.layers as tfkl \n","plt.rc('font', size=16) \n","import random\n","from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n","from sklearn import model_selection\n","from sklearn.metrics import confusion_matrix\n","from sklearn.utils import class_weight\n","from sklearn.preprocessing import MinMaxScaler\n","import warnings\n","import logging\n","from sklearn import preprocessing \n","\n","\n","tfk = tf.keras\n","tfkl = tf.keras.layers\n","print(tf.__version__)"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:39.230086Z","iopub.status.busy":"2022-12-15T12:05:39.229251Z","iopub.status.idle":"2022-12-15T12:05:39.240095Z","shell.execute_reply":"2022-12-15T12:05:39.238266Z","shell.execute_reply.started":"2022-12-15T12:05:39.230044Z"},"id":"AtNSmv7Yu6j_","trusted":true},"outputs":[],"source":["# Random seed for reproducibility\n","seed = 89\n","random.seed(seed)\n","os.environ['PYTHONHASHSEED'] = str(seed)\n","np.random.seed(seed)\n","tf.random.set_seed(seed)\n","tf.compat.v1.set_random_seed(seed)"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:39.241965Z","iopub.status.busy":"2022-12-15T12:05:39.241539Z","iopub.status.idle":"2022-12-15T12:05:39.356256Z","shell.execute_reply":"2022-12-15T12:05:39.355327Z","shell.execute_reply.started":"2022-12-15T12:05:39.241919Z"},"id":"_7sucj6TrC-v","trusted":true},"outputs":[],"source":["path = r'/kaggle/input/dataset-hw2-ann/'\n","x_train = np.load(path + r'x_train.npy')\n","y_train = np.load(path + r'y_train.npy')"]},{"cell_type":"markdown","metadata":{"id":"tZbc9lIi8qJY"},"source":["# Dataset visualization"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:39.359447Z","iopub.status.busy":"2022-12-15T12:05:39.358872Z","iopub.status.idle":"2022-12-15T12:05:39.600709Z","shell.execute_reply":"2022-12-15T12:05:39.599825Z","shell.execute_reply.started":"2022-12-15T12:05:39.359411Z"},"id":"rAprzP2bkm4S","outputId":"05913853-f8b2-4551-b47c-6c4892ed214c","trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# plot labels distribution \n","sns.countplot( x = y_train) \n","plt.title('Dataset Labels Distribution')\n","plt.show()"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:39.602464Z","iopub.status.busy":"2022-12-15T12:05:39.602113Z","iopub.status.idle":"2022-12-15T12:05:39.609543Z","shell.execute_reply":"2022-12-15T12:05:39.608465Z","shell.execute_reply.started":"2022-12-15T12:05:39.602429Z"},"id":"QKDkjdJ7754A","trusted":true},"outputs":[],"source":["# setting up \n","labels = np.unique(np.fromiter([i for i in y_train], np.int32) )\n","class_weights = class_weight.compute_class_weight(class_weight='balanced', classes=labels, y=y_train) "]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:39.612906Z","iopub.status.busy":"2022-12-15T12:05:39.612237Z","iopub.status.idle":"2022-12-15T12:05:40.206587Z","shell.execute_reply":"2022-12-15T12:05:40.205552Z","shell.execute_reply.started":"2022-12-15T12:05:39.612866Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(2429, 117, 6)\n"]}],"source":["from scipy.interpolate import interp1d\n","x_new = [] \n","for el in x_train: \n","    interp = interp1d(x=np.arange(36), y=el, axis=0, kind='cubic')\n","    el = interp(np.arange(0, 35, .3))\n","    x_new.append(el)\n","x_new = np.array(x_new) \n","print(x_new.shape) \n","x_train = x_new"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:40.209092Z","iopub.status.busy":"2022-12-15T12:05:40.207912Z","iopub.status.idle":"2022-12-15T12:05:40.223046Z","shell.execute_reply":"2022-12-15T12:05:40.221837Z","shell.execute_reply.started":"2022-12-15T12:05:40.209051Z"},"id":"iDZWnYE3pP1e","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(1943, 117, 6) (1943,) (486, 117, 6) (486,)\n"]}],"source":["# split \n","x_train, x_test, y_train, y_test = model_selection.train_test_split(x_train, y_train, test_size=0.2, random_state=seed, stratify=y_train) \n","print(x_train.shape, y_train.shape, x_test.shape, y_test.shape )"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:40.236280Z","iopub.status.busy":"2022-12-15T12:05:40.235830Z","iopub.status.idle":"2022-12-15T12:05:40.244925Z","shell.execute_reply":"2022-12-15T12:05:40.244019Z","shell.execute_reply.started":"2022-12-15T12:05:40.236239Z"},"trusted":true},"outputs":[],"source":["y_train = tfk.utils.to_categorical(y_train) \n","y_test = tfk.utils.to_categorical(y_test)"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:40.256462Z","iopub.status.busy":"2022-12-15T12:05:40.256008Z","iopub.status.idle":"2022-12-15T12:05:40.475835Z","shell.execute_reply":"2022-12-15T12:05:40.474898Z","shell.execute_reply.started":"2022-12-15T12:05:40.256427Z"},"id":"3DE3PNbC-dmt","outputId":"4cbe0338-dd27-4941-aae8-2fb85440a7f5","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(227331, 6)\n"]}],"source":["train_data = np.concatenate([x for x in x_train]) \n","print(train_data.shape) \n","scaler = preprocessing.RobustScaler().fit(train_data) #robust scaler best to time, outliers? \n","\n","x_train_trans = [] \n","for el in x_train: \n","    x_train_trans.append(scaler.transform(el))\n","del x_train \n","\n","x_test_trans = [] \n","for el in x_test :\n","    x_test_trans.append(scaler.transform(el))\n","\n","x_train = np.asarray(x_train_trans) \n","x_test = np.asarray(x_test_trans)"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:40.478023Z","iopub.status.busy":"2022-12-15T12:05:40.477170Z","iopub.status.idle":"2022-12-15T12:05:40.484310Z","shell.execute_reply":"2022-12-15T12:05:40.482953Z","shell.execute_reply.started":"2022-12-15T12:05:40.477982Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(1943, 117, 6) (1943, 12)\n"]}],"source":["print(x_train.shape, y_train.shape)"]},{"cell_type":"markdown","metadata":{},"source":["Resampling"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:40.486987Z","iopub.status.busy":"2022-12-15T12:05:40.486654Z","iopub.status.idle":"2022-12-15T12:05:43.944030Z","shell.execute_reply":"2022-12-15T12:05:43.942960Z","shell.execute_reply.started":"2022-12-15T12:05:40.486942Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["2022-12-15 12:05:40.580049: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-12-15 12:05:40.675913: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-12-15 12:05:40.676924: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-12-15 12:05:40.681284: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n","To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n","2022-12-15 12:05:40.681660: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-12-15 12:05:40.682518: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-12-15 12:05:40.683278: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-12-15 12:05:42.895233: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-12-15 12:05:42.896112: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-12-15 12:05:42.896810: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-12-15 12:05:42.897414: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15401 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["(7464, 702) (7464, 12)\n","(7464, 117, 6) (7464, 12)\n"]}],"source":["OLD_SHAPE = x_train.shape[1]\n","x_train_reshaped = tf.reshape(x_train, shape = (x_train.shape[0], x_train.shape[1]*6) )\n","#print(x_train_reshaped.shape, y_train.shape)  \n","from imblearn.over_sampling import RandomOverSampler, SVMSMOTE, SMOTE\n","\n","ros = SMOTE(sampling_strategy='not majority' ,random_state=seed)\n","train_data, labels = ros.fit_resample(x_train_reshaped, y_train)\n","\n","plotting_labels = np.argmax(labels, axis = 1)\n","sns.countplot( y = plotting_labels) \n","plt.title('Dataset Labels Distribution')\n","plt.show()\n","\n","print(train_data.shape, labels.shape) \n","x_res = tf.reshape(train_data, shape = (train_data.shape[0], OLD_SHAPE, 6) ) #PUT NEW SIZE HERE \n","y_res = labels #from above \n","print(x_res.shape, y_res.shape)"]},{"cell_type":"code","execution_count":17,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:43.976284Z","iopub.status.busy":"2022-12-15T12:05:43.975629Z","iopub.status.idle":"2022-12-15T12:05:43.983785Z","shell.execute_reply":"2022-12-15T12:05:43.982905Z","shell.execute_reply.started":"2022-12-15T12:05:43.976249Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(7464, 117, 6) (7464, 12)\n"]}],"source":["X = x_res \n","Y = y_res\n","\n","print(X.shape, Y.shape) \n","input_shape = (OLD_SHAPE,6)"]},{"cell_type":"code","execution_count":18,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:43.985866Z","iopub.status.busy":"2022-12-15T12:05:43.985231Z","iopub.status.idle":"2022-12-15T12:05:44.041063Z","shell.execute_reply":"2022-12-15T12:05:44.040084Z","shell.execute_reply.started":"2022-12-15T12:05:43.985833Z"},"id":"KRI6J1GZuQHw","trusted":true},"outputs":[],"source":["from sklearn.utils import shuffle \n","X = np.reshape(X, X.shape)\n","X, Y = shuffle(X, Y)"]},{"cell_type":"markdown","metadata":{},"source":["# Network"]},{"cell_type":"code","execution_count":19,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:44.042755Z","iopub.status.busy":"2022-12-15T12:05:44.042391Z","iopub.status.idle":"2022-12-15T12:05:44.054938Z","shell.execute_reply":"2022-12-15T12:05:44.054012Z","shell.execute_reply.started":"2022-12-15T12:05:44.042719Z"},"id":"7nxJrPoojt3z","trusted":true},"outputs":[],"source":["def build_ffnn_classifier(input_shape, classes):\n","    input_layer = tfkl.Input(shape=input_shape, name='Input')\n","    x = input_layer\n","        \n","    cnn_1 = tfkl.Conv1D(256,3,padding='same',activation='relu')(x) \n","    cnn = tfkl.MaxPooling1D()(cnn_1)\n","    cnn = tfkl.Conv1D(256,3,padding='same',activation='relu')(cnn)\n","    cnn = tfkl.MaxPooling1D()(cnn)\n","    cnn = tfkl.Conv1D(256,3,padding='same',activation='relu')(cnn)\n","    \n","    x = tfkl.MultiHeadAttention( key_dim=6, num_heads=5, dropout=0.2 )(cnn,tfkl.MaxPool1D(strides = 2)(input_layer) )\n","    x = tfkl.Add()([cnn, tfkl.MaxPool1D(strides = 4)(cnn_1) ])\n","    gap = tfkl.GlobalAveragePooling1D()(x)\n","    dropout = tfkl.Dropout(.3, seed=seed)(gap)\n","\n","\n","    hidden_layer1 = tfkl.Dense(units=256, activation ='relu', name = 'Hidden1', kernel_regularizer = tfk.regularizers.L2(l2 = 0.0003))(dropout) \n","    hidden_layer2 = tfkl.Dense(units=256, activation ='relu', name = 'Hidden2', kernel_regularizer = tfk.regularizers.L2(l2 = 0.0001))(hidden_layer1) \n","    \n","    output_layer = tfkl.Dense(classes, activation='softmax', \n","                              kernel_initializer=tfk.initializers.GlorotUniform(seed))(hidden_layer2)\n","      \n","    model = tfk.Model(inputs=input_layer, outputs=output_layer, name='model')\n","    model.compile(loss=tfk.losses.CategoricalCrossentropy(), optimizer=tfk.optimizers.Adam(), metrics='accuracy')\n","    return model"]},{"cell_type":"code","execution_count":20,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:44.056823Z","iopub.status.busy":"2022-12-15T12:05:44.056377Z","iopub.status.idle":"2022-12-15T12:05:44.223044Z","shell.execute_reply":"2022-12-15T12:05:44.222148Z","shell.execute_reply.started":"2022-12-15T12:05:44.056778Z"},"id":"d1zEtb3Hjt1O","outputId":"345b907a-9e89-41e4-eac4-1b5d544697b1","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","Input (InputLayer)              [(None, 117, 6)]     0                                            \n","__________________________________________________________________________________________________\n","conv1d (Conv1D)                 (None, 117, 256)     4864        Input[0][0]                      \n","__________________________________________________________________________________________________\n","max_pooling1d (MaxPooling1D)    (None, 58, 256)      0           conv1d[0][0]                     \n","__________________________________________________________________________________________________\n","conv1d_1 (Conv1D)               (None, 58, 256)      196864      max_pooling1d[0][0]              \n","__________________________________________________________________________________________________\n","max_pooling1d_1 (MaxPooling1D)  (None, 29, 256)      0           conv1d_1[0][0]                   \n","__________________________________________________________________________________________________\n","conv1d_2 (Conv1D)               (None, 29, 256)      196864      max_pooling1d_1[0][0]            \n","__________________________________________________________________________________________________\n","max_pooling1d_3 (MaxPooling1D)  (None, 29, 256)      0           conv1d[0][0]                     \n","__________________________________________________________________________________________________\n","add (Add)                       (None, 29, 256)      0           conv1d_2[0][0]                   \n","                                                                 max_pooling1d_3[0][0]            \n","__________________________________________________________________________________________________\n","global_average_pooling1d (Globa (None, 256)          0           add[0][0]                        \n","__________________________________________________________________________________________________\n","dropout (Dropout)               (None, 256)          0           global_average_pooling1d[0][0]   \n","__________________________________________________________________________________________________\n","Hidden1 (Dense)                 (None, 256)          65792       dropout[0][0]                    \n","__________________________________________________________________________________________________\n","Hidden2 (Dense)                 (None, 256)          65792       Hidden1[0][0]                    \n","__________________________________________________________________________________________________\n","dense (Dense)                   (None, 12)           3084        Hidden2[0][0]                    \n","==================================================================================================\n","Total params: 533,260\n","Trainable params: 533,260\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"]}],"source":["model = build_ffnn_classifier(input_shape, 12)\n","model.summary()"]},{"cell_type":"code","execution_count":22,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:05:44.232223Z","iopub.status.busy":"2022-12-15T12:05:44.231492Z","iopub.status.idle":"2022-12-15T12:07:50.600861Z","shell.execute_reply":"2022-12-15T12:07:50.599887Z","shell.execute_reply.started":"2022-12-15T12:05:44.232188Z"},"id":"LQOYU2X0jty5","outputId":"366adb62-be1c-4631-c0c3-66db3d5cb982","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/300\n"]},{"name":"stderr","output_type":"stream","text":["2022-12-15 12:05:44.347806: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n","2022-12-15 12:05:46.092947: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005\n"]},{"name":"stdout","output_type":"stream","text":["117/117 [==============================] - 9s 10ms/step - loss: 2.5633 - accuracy: 0.2907 - val_loss: 2.3607 - val_accuracy: 0.2366\n","Epoch 2/300\n","117/117 [==============================] - 1s 6ms/step - loss: 1.9144 - accuracy: 0.4109 - val_loss: 1.9992 - val_accuracy: 0.2901\n","Epoch 3/300\n","117/117 [==============================] - 1s 7ms/step - loss: 1.7473 - accuracy: 0.4649 - val_loss: 1.8516 - val_accuracy: 0.3210\n","Epoch 4/300\n","117/117 [==============================] - 1s 6ms/step - loss: 1.5785 - accuracy: 0.5080 - val_loss: 1.8984 - val_accuracy: 0.3704\n","Epoch 5/300\n","117/117 [==============================] - 1s 8ms/step - loss: 1.4621 - accuracy: 0.5421 - val_loss: 1.7015 - val_accuracy: 0.3909\n","Epoch 6/300\n","117/117 [==============================] - 1s 6ms/step - loss: 1.2765 - accuracy: 0.5856 - val_loss: 1.7031 - val_accuracy: 0.4177\n","Epoch 7/300\n","117/117 [==============================] - 1s 6ms/step - loss: 1.2053 - accuracy: 0.6212 - val_loss: 1.3870 - val_accuracy: 0.4959\n","Epoch 8/300\n","117/117 [==============================] - 1s 6ms/step - loss: 1.1333 - accuracy: 0.6388 - val_loss: 1.5334 - val_accuracy: 0.4959\n","Epoch 9/300\n","117/117 [==============================] - 1s 6ms/step - loss: 1.1418 - accuracy: 0.6392 - val_loss: 1.5928 - val_accuracy: 0.4177\n","Epoch 10/300\n","117/117 [==============================] - 1s 6ms/step - loss: 1.0201 - accuracy: 0.6675 - val_loss: 1.5405 - val_accuracy: 0.5144\n","Epoch 11/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.9198 - accuracy: 0.7014 - val_loss: 1.3621 - val_accuracy: 0.5432\n","Epoch 12/300\n","117/117 [==============================] - 1s 6ms/step - loss: 1.0006 - accuracy: 0.6925 - val_loss: 1.5296 - val_accuracy: 0.4506\n","Epoch 13/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.9025 - accuracy: 0.7168 - val_loss: 1.4591 - val_accuracy: 0.4815\n","Epoch 14/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.9461 - accuracy: 0.7043 - val_loss: 1.5257 - val_accuracy: 0.5041\n","Epoch 15/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.8637 - accuracy: 0.7374 - val_loss: 1.3256 - val_accuracy: 0.5391\n","Epoch 16/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.7339 - accuracy: 0.7712 - val_loss: 1.3713 - val_accuracy: 0.5473\n","Epoch 17/300\n","117/117 [==============================] - 1s 8ms/step - loss: 0.6801 - accuracy: 0.7972 - val_loss: 1.3121 - val_accuracy: 0.5617\n","Epoch 18/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.7472 - accuracy: 0.7819 - val_loss: 1.2995 - val_accuracy: 0.5350\n","Epoch 19/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.6436 - accuracy: 0.8016 - val_loss: 1.3098 - val_accuracy: 0.6008\n","Epoch 20/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.5569 - accuracy: 0.8329 - val_loss: 1.2645 - val_accuracy: 0.6070\n","Epoch 21/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.6194 - accuracy: 0.8165 - val_loss: 1.2187 - val_accuracy: 0.6173\n","Epoch 22/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.5785 - accuracy: 0.8344 - val_loss: 1.1350 - val_accuracy: 0.6831\n","Epoch 23/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.4937 - accuracy: 0.8553 - val_loss: 1.2213 - val_accuracy: 0.6276\n","Epoch 24/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.4430 - accuracy: 0.8734 - val_loss: 1.2753 - val_accuracy: 0.6358\n","Epoch 25/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.5325 - accuracy: 0.8524 - val_loss: 1.2729 - val_accuracy: 0.6049\n","Epoch 26/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.6412 - accuracy: 0.8237 - val_loss: 1.2164 - val_accuracy: 0.6358\n","Epoch 27/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.4844 - accuracy: 0.8644 - val_loss: 1.3158 - val_accuracy: 0.6296\n","Epoch 28/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.4481 - accuracy: 0.8761 - val_loss: 1.3072 - val_accuracy: 0.6914\n","Epoch 29/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.3737 - accuracy: 0.9005 - val_loss: 1.2834 - val_accuracy: 0.6811\n","Epoch 30/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.3577 - accuracy: 0.9089 - val_loss: 1.2727 - val_accuracy: 0.6708\n","Epoch 31/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.3482 - accuracy: 0.9086 - val_loss: 1.2962 - val_accuracy: 0.6543\n","Epoch 32/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.4028 - accuracy: 0.9002 - val_loss: 1.5060 - val_accuracy: 0.6091\n","Epoch 33/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.5483 - accuracy: 0.8589 - val_loss: 1.6170 - val_accuracy: 0.6214\n","Epoch 34/300\n","117/117 [==============================] - 1s 8ms/step - loss: 0.5785 - accuracy: 0.8560 - val_loss: 1.2359 - val_accuracy: 0.6605\n","Epoch 35/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.3723 - accuracy: 0.9026 - val_loss: 1.5204 - val_accuracy: 0.6420\n","Epoch 36/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.3324 - accuracy: 0.9164 - val_loss: 1.5371 - val_accuracy: 0.6523\n","Epoch 37/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.3027 - accuracy: 0.9259 - val_loss: 1.3494 - val_accuracy: 0.6996\n","Epoch 38/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2970 - accuracy: 0.9297 - val_loss: 1.4670 - val_accuracy: 0.7202\n","Epoch 39/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2807 - accuracy: 0.9370 - val_loss: 1.3871 - val_accuracy: 0.7037\n","Epoch 40/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2812 - accuracy: 0.9381 - val_loss: 1.4201 - val_accuracy: 0.6996\n","Epoch 41/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2676 - accuracy: 0.9382 - val_loss: 1.3825 - val_accuracy: 0.7016\n","Epoch 42/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2363 - accuracy: 0.9477 - val_loss: 1.6073 - val_accuracy: 0.6811\n","Epoch 43/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2183 - accuracy: 0.9539 - val_loss: 1.4360 - val_accuracy: 0.7140\n","Epoch 44/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2006 - accuracy: 0.9613 - val_loss: 1.6095 - val_accuracy: 0.6914\n","Epoch 45/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2171 - accuracy: 0.9567 - val_loss: 2.1728 - val_accuracy: 0.6708\n","Epoch 46/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2505 - accuracy: 0.9444 - val_loss: 1.6262 - val_accuracy: 0.6955\n","Epoch 47/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2007 - accuracy: 0.9574 - val_loss: 1.6087 - val_accuracy: 0.6708\n","Epoch 48/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.1920 - accuracy: 0.9624 - val_loss: 1.5173 - val_accuracy: 0.7346\n","Epoch 49/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.1832 - accuracy: 0.9637 - val_loss: 1.5936 - val_accuracy: 0.7202\n","Epoch 50/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1912 - accuracy: 0.9625 - val_loss: 1.5821 - val_accuracy: 0.6975\n","Epoch 51/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1985 - accuracy: 0.9607 - val_loss: 1.7133 - val_accuracy: 0.6852\n","Epoch 52/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.3411 - accuracy: 0.9227 - val_loss: 1.4259 - val_accuracy: 0.6646\n","Epoch 53/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2404 - accuracy: 0.9535 - val_loss: 1.4427 - val_accuracy: 0.6914\n","Epoch 54/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2804 - accuracy: 0.9424 - val_loss: 1.5355 - val_accuracy: 0.6605\n","Epoch 55/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.3033 - accuracy: 0.9310 - val_loss: 1.5997 - val_accuracy: 0.6687\n","Epoch 56/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.2145 - accuracy: 0.9594 - val_loss: 1.5485 - val_accuracy: 0.7016\n","Epoch 57/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.3279 - accuracy: 0.9404 - val_loss: 1.7801 - val_accuracy: 0.6543\n","Epoch 58/300\n","117/117 [==============================] - 1s 8ms/step - loss: 0.2675 - accuracy: 0.9447 - val_loss: 1.6332 - val_accuracy: 0.6811\n","Epoch 59/300\n","117/117 [==============================] - 1s 8ms/step - loss: 0.3120 - accuracy: 0.9429 - val_loss: 2.1409 - val_accuracy: 0.6276\n","Epoch 60/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2817 - accuracy: 0.9427 - val_loss: 1.4756 - val_accuracy: 0.6955\n","Epoch 61/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1869 - accuracy: 0.9652 - val_loss: 1.9535 - val_accuracy: 0.6749\n","Epoch 62/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2423 - accuracy: 0.9514 - val_loss: 1.9287 - val_accuracy: 0.6646\n","Epoch 63/300\n","117/117 [==============================] - 1s 8ms/step - loss: 0.3024 - accuracy: 0.9370 - val_loss: 1.5504 - val_accuracy: 0.7099\n","Epoch 64/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1887 - accuracy: 0.9656 - val_loss: 1.7206 - val_accuracy: 0.7078\n","Epoch 65/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1467 - accuracy: 0.9767 - val_loss: 1.7771 - val_accuracy: 0.7428\n","Epoch 66/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1345 - accuracy: 0.9823 - val_loss: 1.9050 - val_accuracy: 0.7037\n","Epoch 67/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.1286 - accuracy: 0.9847 - val_loss: 2.0564 - val_accuracy: 0.7078\n","Epoch 68/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1317 - accuracy: 0.9822 - val_loss: 1.9091 - val_accuracy: 0.7058\n","Epoch 69/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1365 - accuracy: 0.9807 - val_loss: 1.7708 - val_accuracy: 0.7202\n","Epoch 70/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1210 - accuracy: 0.9857 - val_loss: 2.2228 - val_accuracy: 0.7407\n","Epoch 71/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1515 - accuracy: 0.9778 - val_loss: 1.6918 - val_accuracy: 0.7037\n","Epoch 72/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1312 - accuracy: 0.9804 - val_loss: 1.7681 - val_accuracy: 0.7140\n","Epoch 73/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1266 - accuracy: 0.9847 - val_loss: 1.8080 - val_accuracy: 0.7305\n","Epoch 74/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1122 - accuracy: 0.9879 - val_loss: 1.9814 - val_accuracy: 0.7284\n","Epoch 75/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1290 - accuracy: 0.9811 - val_loss: 2.2850 - val_accuracy: 0.7140\n","Epoch 76/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1232 - accuracy: 0.9827 - val_loss: 1.7945 - val_accuracy: 0.7325\n","Epoch 77/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.1199 - accuracy: 0.9834 - val_loss: 1.7327 - val_accuracy: 0.7202\n","Epoch 78/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.1093 - accuracy: 0.9857 - val_loss: 2.0761 - val_accuracy: 0.7099\n","Epoch 79/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1781 - accuracy: 0.9721 - val_loss: 2.1130 - val_accuracy: 0.7243\n","Epoch 80/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1974 - accuracy: 0.9629 - val_loss: 1.6692 - val_accuracy: 0.7058\n","Epoch 81/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1220 - accuracy: 0.9826 - val_loss: 1.9388 - val_accuracy: 0.7284\n","Epoch 82/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0983 - accuracy: 0.9900 - val_loss: 2.1078 - val_accuracy: 0.7325\n","Epoch 83/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0897 - accuracy: 0.9925 - val_loss: 1.9790 - val_accuracy: 0.7305\n","Epoch 84/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0980 - accuracy: 0.9886 - val_loss: 1.8646 - val_accuracy: 0.7099\n","Epoch 85/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0979 - accuracy: 0.9882 - val_loss: 2.0962 - val_accuracy: 0.7387\n","Epoch 86/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2495 - accuracy: 0.9589 - val_loss: 2.2705 - val_accuracy: 0.6399\n","Epoch 87/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.2188 - accuracy: 0.9654 - val_loss: 1.6857 - val_accuracy: 0.7037\n","Epoch 88/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1478 - accuracy: 0.9725 - val_loss: 1.7634 - val_accuracy: 0.7202\n","Epoch 89/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1327 - accuracy: 0.9818 - val_loss: 1.9182 - val_accuracy: 0.7263\n","Epoch 90/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1055 - accuracy: 0.9874 - val_loss: 1.8502 - val_accuracy: 0.7078\n","Epoch 91/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0952 - accuracy: 0.9893 - val_loss: 2.1736 - val_accuracy: 0.7016\n","Epoch 92/300\n","117/117 [==============================] - 1s 8ms/step - loss: 0.1164 - accuracy: 0.9808 - val_loss: 1.7445 - val_accuracy: 0.7284\n","Epoch 93/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1902 - accuracy: 0.9668 - val_loss: 2.0113 - val_accuracy: 0.6831\n","Epoch 94/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.1042 - accuracy: 0.9875 - val_loss: 1.9843 - val_accuracy: 0.7140\n","Epoch 95/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0889 - accuracy: 0.9910 - val_loss: 2.1210 - val_accuracy: 0.7202\n","Epoch 96/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0819 - accuracy: 0.9949 - val_loss: 1.9749 - val_accuracy: 0.7325\n","Epoch 97/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0700 - accuracy: 0.9973 - val_loss: 2.0040 - val_accuracy: 0.7428\n","Epoch 98/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.0692 - accuracy: 0.9977 - val_loss: 2.0757 - val_accuracy: 0.7387\n","Epoch 99/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.0678 - accuracy: 0.9977 - val_loss: 2.1428 - val_accuracy: 0.7469\n","Epoch 100/300\n","117/117 [==============================] - 1s 8ms/step - loss: 0.0684 - accuracy: 0.9971 - val_loss: 2.1062 - val_accuracy: 0.7428\n","Epoch 101/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0669 - accuracy: 0.9976 - val_loss: 2.1540 - val_accuracy: 0.7387\n","Epoch 102/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0662 - accuracy: 0.9979 - val_loss: 2.1585 - val_accuracy: 0.7305\n","Epoch 103/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0705 - accuracy: 0.9973 - val_loss: 2.4163 - val_accuracy: 0.7140\n","Epoch 104/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.0745 - accuracy: 0.9957 - val_loss: 2.2036 - val_accuracy: 0.7181\n","Epoch 105/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.0673 - accuracy: 0.9971 - val_loss: 2.2829 - val_accuracy: 0.7428\n","Epoch 106/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.0648 - accuracy: 0.9975 - val_loss: 2.4510 - val_accuracy: 0.7284\n","Epoch 107/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.0654 - accuracy: 0.9980 - val_loss: 2.3752 - val_accuracy: 0.7366\n","Epoch 108/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0719 - accuracy: 0.9972 - val_loss: 2.1539 - val_accuracy: 0.7181\n","Epoch 109/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0649 - accuracy: 0.9976 - val_loss: 2.3230 - val_accuracy: 0.7428\n","Epoch 110/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0645 - accuracy: 0.9976 - val_loss: 2.3458 - val_accuracy: 0.7428\n","Epoch 111/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0611 - accuracy: 0.9980 - val_loss: 2.3569 - val_accuracy: 0.7428\n","Epoch 112/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0587 - accuracy: 0.9989 - val_loss: 2.4601 - val_accuracy: 0.7469\n","Epoch 113/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0584 - accuracy: 0.9988 - val_loss: 2.4289 - val_accuracy: 0.7325\n","Epoch 114/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0590 - accuracy: 0.9983 - val_loss: 2.4944 - val_accuracy: 0.7490\n","Epoch 115/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.0580 - accuracy: 0.9983 - val_loss: 2.4513 - val_accuracy: 0.7428\n","Epoch 116/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0591 - accuracy: 0.9981 - val_loss: 2.5567 - val_accuracy: 0.7654\n","Epoch 117/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.0559 - accuracy: 0.9989 - val_loss: 2.4087 - val_accuracy: 0.7407\n","Epoch 118/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0546 - accuracy: 0.9989 - val_loss: 2.5082 - val_accuracy: 0.7387\n","Epoch 119/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.0621 - accuracy: 0.9977 - val_loss: 2.4523 - val_accuracy: 0.7284\n","Epoch 120/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0627 - accuracy: 0.9958 - val_loss: 2.4337 - val_accuracy: 0.7613\n","Epoch 121/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.0584 - accuracy: 0.9969 - val_loss: 2.3927 - val_accuracy: 0.7572\n","Epoch 122/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0536 - accuracy: 0.9992 - val_loss: 2.3801 - val_accuracy: 0.7531\n","Epoch 123/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0545 - accuracy: 0.9981 - val_loss: 2.5274 - val_accuracy: 0.7407\n","Epoch 124/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0533 - accuracy: 0.9984 - val_loss: 2.3955 - val_accuracy: 0.7325\n","Epoch 125/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0529 - accuracy: 0.9983 - val_loss: 2.4015 - val_accuracy: 0.7325\n","Epoch 126/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0521 - accuracy: 0.9981 - val_loss: 2.4375 - val_accuracy: 0.7490\n","Epoch 127/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0507 - accuracy: 0.9991 - val_loss: 2.4036 - val_accuracy: 0.7469\n","Epoch 128/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0562 - accuracy: 0.9979 - val_loss: 2.2344 - val_accuracy: 0.7387\n","Epoch 129/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0508 - accuracy: 0.9979 - val_loss: 2.3679 - val_accuracy: 0.7469\n","Epoch 130/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0514 - accuracy: 0.9977 - val_loss: 2.2597 - val_accuracy: 0.7366\n","Epoch 131/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0496 - accuracy: 0.9981 - val_loss: 2.3701 - val_accuracy: 0.7305\n","Epoch 132/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0499 - accuracy: 0.9980 - val_loss: 2.4510 - val_accuracy: 0.7366\n","Epoch 133/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0476 - accuracy: 0.9989 - val_loss: 2.4183 - val_accuracy: 0.7551\n","Epoch 134/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0464 - accuracy: 0.9991 - val_loss: 2.4877 - val_accuracy: 0.7634\n","Epoch 135/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.0455 - accuracy: 0.9988 - val_loss: 2.4742 - val_accuracy: 0.7551\n","Epoch 136/300\n","117/117 [==============================] - 1s 8ms/step - loss: 0.0468 - accuracy: 0.9983 - val_loss: 2.4529 - val_accuracy: 0.7490\n","Epoch 137/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0454 - accuracy: 0.9985 - val_loss: 2.4518 - val_accuracy: 0.7387\n","Epoch 138/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0444 - accuracy: 0.9985 - val_loss: 2.4855 - val_accuracy: 0.7531\n","Epoch 139/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0431 - accuracy: 0.9992 - val_loss: 2.3063 - val_accuracy: 0.7407\n","Epoch 140/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0437 - accuracy: 0.9988 - val_loss: 2.4969 - val_accuracy: 0.7510\n","Epoch 141/300\n","117/117 [==============================] - 1s 8ms/step - loss: 0.0447 - accuracy: 0.9984 - val_loss: 2.6271 - val_accuracy: 0.7243\n","Epoch 142/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.0428 - accuracy: 0.9983 - val_loss: 2.9532 - val_accuracy: 0.7531\n","Epoch 143/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.0498 - accuracy: 0.9960 - val_loss: 2.4606 - val_accuracy: 0.7490\n","Epoch 144/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0435 - accuracy: 0.9981 - val_loss: 2.5202 - val_accuracy: 0.7325\n","Epoch 145/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0422 - accuracy: 0.9987 - val_loss: 2.5164 - val_accuracy: 0.7407\n","Epoch 146/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0431 - accuracy: 0.9981 - val_loss: 2.4890 - val_accuracy: 0.7222\n","Epoch 147/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0436 - accuracy: 0.9980 - val_loss: 2.5503 - val_accuracy: 0.7449\n","Epoch 148/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0392 - accuracy: 0.9993 - val_loss: 2.5195 - val_accuracy: 0.7490\n","Epoch 149/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0390 - accuracy: 0.9991 - val_loss: 2.4884 - val_accuracy: 0.7387\n","Epoch 150/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.0395 - accuracy: 0.9988 - val_loss: 2.5111 - val_accuracy: 0.7387\n","Epoch 151/300\n","117/117 [==============================] - 1s 7ms/step - loss: 0.0379 - accuracy: 0.9996 - val_loss: 2.5577 - val_accuracy: 0.7490\n","Epoch 152/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0384 - accuracy: 0.9992 - val_loss: 2.5650 - val_accuracy: 0.7469\n","Epoch 153/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0390 - accuracy: 0.9989 - val_loss: 2.5203 - val_accuracy: 0.7469\n","Epoch 154/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0391 - accuracy: 0.9991 - val_loss: 2.5409 - val_accuracy: 0.7407\n","Epoch 155/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0385 - accuracy: 0.9993 - val_loss: 2.5403 - val_accuracy: 0.7469\n","Epoch 156/300\n","117/117 [==============================] - 1s 6ms/step - loss: 0.0390 - accuracy: 0.9989 - val_loss: 2.5534 - val_accuracy: 0.7387\n"]}],"source":["history1 = model.fit(\n","    x = X,\n","    y = Y,\n","    batch_size = 64,\n","    epochs = 300,\n","    validation_data = (x_test, y_test),\n","    callbacks = [\n","        tfk.callbacks.EarlyStopping(monitor='val_accuracy', mode='max', patience=40, restore_best_weights=True),\n","        tfk.callbacks.ReduceLROnPlateau(monitor='val_accuracy', mode='max', patience=30, factor=0.2, min_lr=1e-5)\n","    ]\n",").history"]},{"cell_type":"code","execution_count":23,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:07:50.604379Z","iopub.status.busy":"2022-12-15T12:07:50.604058Z","iopub.status.idle":"2022-12-15T12:08:07.944345Z","shell.execute_reply":"2022-12-15T12:08:07.943279Z","shell.execute_reply.started":"2022-12-15T12:07:50.604338Z"},"id":"rzrqDHgijtwW","outputId":"09520c66-4c4b-4b97-b7df-654f6172c6f7","trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure 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","text/plain":["<Figure 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","text/plain":["<Figure size 1296x216 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["best_epoch = np.argmax(history1['val_accuracy'])\n","plt.figure(figsize=(17,4))\n","plt.plot(history1['loss'], label='Training loss', alpha=.8, color='#ff7f0e')\n","plt.plot(history1['val_loss'], label='Validation loss', alpha=.9, color='#5a9aa5')\n","plt.axvline(x=best_epoch, label='Best epoch', alpha=.3, ls='--', color='#5a9aa5')\n","plt.title('Categorical Crossentropy')\n","plt.legend()\n","plt.grid(alpha=.3)\n","plt.show()\n","\n","plt.figure(figsize=(17,4))\n","plt.plot(history1['accuracy'], label='Training accuracy', alpha=.8, color='#ff7f0e')\n","plt.plot(history1['val_accuracy'], label='Validation accuracy', alpha=.9, color='#5a9aa5')\n","plt.axvline(x=best_epoch, label='Best epoch', alpha=.3, ls='--', color='#5a9aa5')\n","plt.title('Accuracy')\n","plt.legend()\n","plt.grid(alpha=.3)\n","plt.show()\n","\n","plt.figure(figsize=(18,3))\n","plt.plot(history1['lr'], label='Learning Rate', alpha=.8, color='#ff7f0e')\n","plt.axvline(x=best_epoch, label='Best epoch', alpha=.3, ls='--', color='#5a9aa5')\n","plt.legend()\n","plt.grid(alpha=.3)\n","plt.show()"]},{"cell_type":"code","execution_count":24,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:08:07.951505Z","iopub.status.busy":"2022-12-15T12:08:07.948637Z","iopub.status.idle":"2022-12-15T12:08:09.627470Z","shell.execute_reply":"2022-12-15T12:08:09.626388Z","shell.execute_reply.started":"2022-12-15T12:08:07.951464Z"},"id":"KRdpbCqCjtpR","outputId":"d94c6944-6a61-44d8-ff1f-12ca0c2d2821","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["16/16 [==============================] - 0s 2ms/step - loss: 0.0510 - accuracy: 0.0000e+00\n","{'loss': 0.05104183778166771, 'accuracy': 0.0}\n","Accuracy: 0.7654\n","Precision: 0.7009\n","Recall: 0.6165\n","F1: 0.637\n","F1 (per class): [0.6        0.74074074 0.95327103 0.55474453 0.31578947 0.44897959\n"," 0.92307692 0.47619048 0.875      0.81920904 0.9375     0.        ]\n"]},{"data":{"image/png":"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","text/plain":["<Figure 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","text/plain":["<Figure size 720x576 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["metrics = model.evaluate(x_test, return_dict=True)\n","print(metrics)\n","\n","labels = y_test \n","predictions = model.predict(x_test) \n","assert len(labels) == len(predictions)\n","\n","# Compute the confusion matrix\n","cm = confusion_matrix(np.argmax(labels, axis=-1), np.argmax(predictions, axis=-1))\n","\n","# Compute the classification metrics\n","accuracy = accuracy_score(np.argmax(labels, axis=-1), np.argmax(predictions, axis=-1))\n","precision = precision_score(np.argmax(labels, axis=-1), np.argmax(predictions, axis=-1), average='macro')\n","recall = recall_score(np.argmax(labels, axis=-1), np.argmax(predictions, axis=-1), average='macro')\n","f1 = f1_score(np.argmax(labels, axis=-1), np.argmax(predictions, axis=-1), average='macro')\n","f1_classes = f1_score(np.argmax(labels, axis=-1), np.argmax(predictions, axis=-1), average=None)\n","\n","print('Accuracy:',accuracy.round(4))\n","print('Precision:',precision.round(4))\n","print('Recall:',recall.round(4))\n","print('F1:',f1.round(4))\n","print('F1 (per class):' ,f1_classes)\n","\n","# Plot the confusion matrix\n","ig, ax = plt.subplots(figsize=(12,12))\n","ax.matshow(cm, cmap=plt.cm.Blues, alpha=0.3)\n","for i in range(cm.shape[0]):\n","    for j in range(cm.shape[1]):\n","        ax.text(x=j, y=i,s=cm[i, j], va='center', ha='center', size='xx-large')\n","\n","        \n","plt.figure(figsize=(10,8))\n","sns.heatmap(cm.T, cmap='Blues')\n","plt.xlabel('True labels')\n","plt.ylabel('Predicted labels')\n","plt.show()"]},{"cell_type":"code","execution_count":25,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:08:09.629699Z","iopub.status.busy":"2022-12-15T12:08:09.629302Z","iopub.status.idle":"2022-12-15T12:08:11.716646Z","shell.execute_reply":"2022-12-15T12:08:11.714998Z","shell.execute_reply.started":"2022-12-15T12:08:09.629657Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["2022-12-15 12:08:10.334992: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n","/opt/conda/lib/python3.7/site-packages/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n","  category=CustomMaskWarning)\n"]}],"source":["model.save('sub5')"]},{"cell_type":"code","execution_count":26,"metadata":{"execution":{"iopub.execute_input":"2022-12-15T12:08:11.720276Z","iopub.status.busy":"2022-12-15T12:08:11.719238Z","iopub.status.idle":"2022-12-15T12:08:11.725545Z","shell.execute_reply":"2022-12-15T12:08:11.724359Z","shell.execute_reply.started":"2022-12-15T12:08:11.720235Z"},"trusted":true},"outputs":[],"source":["import pickle \n","with open(r'scaler', 'wb') as f: \n","    pickle.dump(scaler, f)"]}],"metadata":{"kernelspec":{"display_name":"Python 3.9.2 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.2"},"vscode":{"interpreter":{"hash":"916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"}}},"nbformat":4,"nbformat_minor":4}