brain-tumor-segmentation-attention-u-net-98.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":[{"sourceId":1299795,"sourceType":"datasetVersion","datasetId":751906}],"dockerImageVersionId":30762,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.models import Model, load_model\nfrom tensorflow.keras.layers import Input, BatchNormalization, Activation, Conv2D, UpSampling2D, MaxPooling2D, concatenate\nfrom tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\nfrom tensorflow.keras import optimizers\nimport os\nimport nibabel as nib\nimport cv2 as cv\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras import backend as K\nimport glob\nimport skimage.io as io\nimport skimage.color as color\nimport random as r\nimport math\nfrom nilearn import plotting\nimport warnings\nwarnings.filterwarnings('ignore')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-09-10T07:00:43.589984Z","iopub.execute_input":"2024-09-10T07:00:43.590303Z","iopub.status.idle":"2024-09-10T07:00:59.240615Z","shell.execute_reply.started":"2024-09-10T07:00:43.590254Z","shell.execute_reply":"2024-09-10T07:00:59.239784Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"Flair= nib.load('../input/brats20-dataset-training-validation/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/BraTS20_Training_003/BraTS20_Training_003_flair.nii')\nSeg= nib.load('../input/brats20-dataset-training-validation/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/BraTS20_Training_003/BraTS20_Training_003_seg.nii')\nT1= nib.load('../input/brats20-dataset-training-validation/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/BraTS20_Training_003/BraTS20_Training_003_t1.nii')\nT1ce= nib.load('../input/brats20-dataset-training-validation/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/BraTS20_Training_003/BraTS20_Training_003_t1ce.nii')\nT2= nib.load('../input/brats20-dataset-training-validation/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/BraTS20_Training_003/BraTS20_Training_003_t2.nii')","metadata":{"execution":{"iopub.status.busy":"2024-09-10T07:00:59.242148Z","iopub.execute_input":"2024-09-10T07:00:59.242735Z","iopub.status.idle":"2024-09-10T07:00:59.352310Z","shell.execute_reply.started":"2024-09-10T07:00:59.242697Z","shell.execute_reply":"2024-09-10T07:00:59.351432Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"# Path to the dataset\ndata_path = '../input/brats20-dataset-training-validation/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData'\npatients = os.listdir(data_path)\n\n# Initializing list to hold preprocessed data\nprocessed_data = []\n\n# Function to preprocess the modalities for a single patient\ndef preprocess_modalities(modality_paths):\n    modalities = []\n    \n    # Loop through each modality file and load it\n    for modality in modality_paths:\n        # Load the NIfTI file and convert to a NumPy array\n        nifti_image = nib.load(modality)\n        modality_data = np.asarray(nifti_image.dataobj)\n        modalities.append(modality_data)\n    \n    # Stacking modalities into a single array and transpose to match the expected input shape\n    stacked_modalities = np.stack(modalities)\n    \n    # Converting data to integer type\n    stacked_modalities = np.rint(stacked_modalities).astype(np.int16)\n    \n    # Transposing the stacked array so that dimensions match (H, W, D, Channels)\n    stacked_modalities = np.transpose(stacked_modalities, (1, 2, 3, 0))\n    \n    return stacked_modalities\n\n# Iterate over the first 20 patients and process their data\nfor patient in patients[:20]:\n    patient_dir = os.path.join(data_path, patient)\n    \n    # Gather all modality file paths\n    flair_path = glob.glob(os.path.join(patient_dir, '*_flair*.nii'))[0]\n    t1_path = glob.glob(os.path.join(patient_dir, '*_t1*.nii'))[0]\n    t1ce_path = glob.glob(os.path.join(patient_dir, '*_t1ce*.nii'))[0]\n    t2_path = glob.glob(os.path.join(patient_dir, '*_t2*.nii'))[0]\n    ground_truth_path = glob.glob(os.path.join(patient_dir, '*_seg*.nii'))[0]\n    \n    # Store the paths for the different modalities\n    modality_paths = [flair_path, t1_path, t1ce_path, t2_path, ground_truth_path]\n    \n    # Preprocess and add to the data list\n    patient_data = preprocess_modalities(modality_paths)\n    processed_data.append(patient_data)","metadata":{"execution":{"iopub.status.busy":"2024-09-10T07:00:59.353323Z","iopub.execute_input":"2024-09-10T07:00:59.353619Z","iopub.status.idle":"2024-09-10T07:01:22.686387Z","shell.execute_reply.started":"2024-09-10T07:00:59.353585Z","shell.execute_reply":"2024-09-10T07:01:22.685540Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"# Function to concatenate modalities along the 0th axis\ndef concatenate_modalities(input_data):\n    concatenated_modalities = []\n    \n    # Iterating over each modality (there are 5 modalities per patient)\n    for modality_index in range(5):\n        print('$')\n        concatenated_modality = None\n        \n        for i in range(0, len(input_data), 2):\n            # Extract the modality from each patient\n            modality_a = input_data[i][:, :, :, modality_index]\n            modality_b = input_data[i + 1][:, :, :, modality_index]\n            \n            if i == 0:\n                concatenated_modality = np.concatenate((modality_a, modality_b), axis=0)\n                print('Initial concatenation: {}'.format(concatenated_modality.shape))\n            else:\n                # Concatenate with the previous concatenated result\n                concatenated_pair = np.concatenate((modality_a, modality_b), axis=0)\n                concatenated_modality = np.concatenate((concatenated_modality, concatenated_pair), axis=0)\n                print('Further concatenation: {}'.format(concatenated_modality.shape))\n        \n       \n        concatenated_modality = concatenated_modality[:, :, :, np.newaxis]\n        concatenated_modalities.append(concatenated_modality)\n    \n    return concatenated_modalities\n\nconcatenated_modalities = concatenate_modalities(processed_data)\n\ncombined_modalities = np.concatenate(concatenated_modalities, axis=3)\n\ncombined_modalities = np.array(combined_modalities, dtype='float32')\n\nt1_modality = np.array(combined_modalities[:, :, :, 1], dtype='float32')\nground_truth_labels = np.array(combined_modalities[:, :, :, 4], dtype='float32')\n\n# Checking the shapes of the arrays\nprint(\"Shape of combined_modalities: \", combined_modalities.shape)\nprint(\"Shape of t1_modality: \", t1_modality.shape)\nprint(\"Shape of ground_truth_labels: \", ground_truth_labels.shape)","metadata":{"execution":{"iopub.status.busy":"2024-09-10T07:01:22.688979Z","iopub.execute_input":"2024-09-10T07:01:22.689387Z","iopub.status.idle":"2024-09-10T07:01:29.468637Z","shell.execute_reply.started":"2024-09-10T07:01:22.689343Z","shell.execute_reply":"2024-09-10T07:01:29.467681Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"$\nInitial concatenation: (480, 240, 155)\nFurther concatenation: (960, 240, 155)\nFurther concatenation: (1440, 240, 155)\nFurther concatenation: (1920, 240, 155)\nFurther concatenation: (2400, 240, 155)\nFurther concatenation: (2880, 240, 155)\nFurther concatenation: (3360, 240, 155)\nFurther concatenation: (3840, 240, 155)\nFurther concatenation: (4320, 240, 155)\nFurther concatenation: (4800, 240, 155)\n$\nInitial concatenation: (480, 240, 155)\nFurther concatenation: (960, 240, 155)\nFurther concatenation: (1440, 240, 155)\nFurther concatenation: (1920, 240, 155)\nFurther concatenation: (2400, 240, 155)\nFurther concatenation: (2880, 240, 155)\nFurther concatenation: (3360, 240, 155)\nFurther concatenation: (3840, 240, 155)\nFurther concatenation: (4320, 240, 155)\nFurther concatenation: (4800, 240, 155)\n$\nInitial concatenation: (480, 240, 155)\nFurther concatenation: (960, 240, 155)\nFurther concatenation: (1440, 240, 155)\nFurther concatenation: (1920, 240, 155)\nFurther concatenation: (2400, 240, 155)\nFurther concatenation: (2880, 240, 155)\nFurther concatenation: (3360, 240, 155)\nFurther concatenation: (3840, 240, 155)\nFurther concatenation: (4320, 240, 155)\nFurther concatenation: (4800, 240, 155)\n$\nInitial concatenation: (480, 240, 155)\nFurther concatenation: (960, 240, 155)\nFurther concatenation: (1440, 240, 155)\nFurther concatenation: (1920, 240, 155)\nFurther concatenation: (2400, 240, 155)\nFurther concatenation: (2880, 240, 155)\nFurther concatenation: (3360, 240, 155)\nFurther concatenation: (3840, 240, 155)\nFurther concatenation: (4320, 240, 155)\nFurther concatenation: (4800, 240, 155)\n$\nInitial concatenation: (480, 240, 155)\nFurther concatenation: (960, 240, 155)\nFurther concatenation: (1440, 240, 155)\nFurther concatenation: (1920, 240, 155)\nFurther concatenation: (2400, 240, 155)\nFurther concatenation: (2880, 240, 155)\nFurther concatenation: (3360, 240, 155)\nFurther concatenation: (3840, 240, 155)\nFurther concatenation: (4320, 240, 155)\nFurther concatenation: (4800, 240, 155)\nShape of combined_modalities:  (4800, 240, 155, 5)\nShape of t1_modality:  (4800, 240, 155)\nShape of ground_truth_labels:  (4800, 240, 155)\n","output_type":"stream"}]},{"cell_type":"code","source":"# Function to visualize a slice from different modalities of a patient\ndef visualize_modalities(patient_data, slice_index):\n    modalities = ['FLAIR', 'T1', 'T1CE', 'T2', 'Ground Truth']\n    \n    fig, axes = plt.subplots(1, 5, figsize=(20, 5))\n    \n    for i in range(5):\n        axes[i].imshow(patient_data[:, :, slice_index, i], cmap='gray')\n        axes[i].set_title(modalities[i])\n        axes[i].axis('off')\n    \n    plt.show()\n\nselected_patient = processed_data[1]\n\nslice_to_visualize = 80\n\n# Visualize the modalities for the selected patient and slice\nvisualize_modalities(selected_patient, slice_to_visualize)","metadata":{"execution":{"iopub.status.busy":"2024-09-10T07:01:29.470019Z","iopub.execute_input":"2024-09-10T07:01:29.470435Z","iopub.status.idle":"2024-09-10T07:01:29.964622Z","shell.execute_reply.started":"2024-09-10T07:01:29.470389Z","shell.execute_reply":"2024-09-10T07:01:29.963592Z"},"trusted":true},"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x500 with 5 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\n# 80% Training, 10% Test, 10% Validation Split\n# First, split into 90% train and 10% temp (test+validation)\nX_train, X_temp, Y_train, Y_temp = train_test_split(t1_modality, ground_truth_labels, test_size=0.40, random_state=42)\n\nX_test, X_val, Y_test, Y_val = train_test_split(X_temp, Y_temp, test_size=0.5, random_state=42)\n\ncombined_modalities = 0 \n\nprint(f\"Training set: X_train: {X_train.shape}, Y_train: {Y_train.shape}\")\nprint(f\"Validation set: X_val: {X_val.shape}, Y_val: {Y_val.shape}\")\nprint(f\"Test set: X_test: {X_test.shape}, Y_test: {Y_test.shape}\")","metadata":{"execution":{"iopub.status.busy":"2024-09-10T07:06:35.410704Z","iopub.execute_input":"2024-09-10T07:06:35.411451Z","iopub.status.idle":"2024-09-10T07:06:39.550151Z","shell.execute_reply.started":"2024-09-10T07:06:35.411412Z","shell.execute_reply":"2024-09-10T07:06:39.549116Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Training set: X_train: (2880, 240, 155), Y_train: (2880, 240, 155)\nValidation set: X_val: (960, 240, 155), Y_val: (960, 240, 155)\nTest set: X_test: (960, 240, 155), Y_test: (960, 240, 155)\n","output_type":"stream"}]},{"cell_type":"code","source":"from skimage.transform import resize\nfrom tensorflow.keras import layers, models\n\n# Resizing images to match model input shape\ndef resize_images(images, target_size):\n    resized_images = []\n    for image in images:\n        resized_image = resize(image, target_size, mode='reflect', anti_aliasing=True)\n        resized_images.append(resized_image)\n    return np.array(resized_images)\n\n# Defining Attention U-Net model\ndef attention_unet_model(input_shape):\n    inputs = layers.Input(shape=input_shape)\n    \n    # Encoder\n    conv1 = layers.Conv2D(64, 3, activation='relu', padding='same')(inputs)\n    conv1 = layers.Conv2D(64, 3, activation='relu', padding='same')(conv1)\n    pool1 = layers.MaxPooling2D(pool_size=(2, 2))(conv1)\n    \n    conv2 = layers.Conv2D(128, 3, activation='relu', padding='same')(pool1)\n    conv2 = layers.Conv2D(128, 3, activation='relu', padding='same')(conv2)\n    pool2 = layers.MaxPooling2D(pool_size=(2, 2))(conv2)\n    \n    conv3 = layers.Conv2D(256, 3, activation='relu', padding='same')(pool2)\n    conv3 = layers.Conv2D(256, 3, activation='relu', padding='same')(conv3)\n    pool3 = layers.MaxPooling2D(pool_size=(2, 2))(conv3)\n    \n    conv4 = layers.Conv2D(512, 3, activation='relu', padding='same')(pool3)\n    conv4 = layers.Conv2D(512, 3, activation='relu', padding='same')(conv4)\n    pool4 = layers.MaxPooling2D(pool_size=(2, 2))(conv4)\n    \n    # Bottleneck\n    conv5 = layers.Conv2D(1024, 3, activation='relu', padding='same')(pool4)\n    conv5 = layers.Conv2D(1024, 3, activation='relu', padding='same')(conv5)\n    \n    # Decoder\n    up6 = layers.Conv2DTranspose(512, 2, strides=(2, 2), padding='same')(conv5)\n    concat6 = layers.Concatenate()([up6, conv4])\n    conv6 = layers.Conv2D(512, 3, activation='relu', padding='same')(concat6)\n    conv6 = layers.Conv2D(512, 3, activation='relu', padding='same')(conv6)\n    \n    up7 = layers.Conv2DTranspose(256, 2, strides=(2, 2), padding='same')(conv6)\n    concat7 = layers.Concatenate()([up7, conv3])\n    conv7 = layers.Conv2D(256, 3, activation='relu', padding='same')(concat7)\n    conv7 = layers.Conv2D(256, 3, activation='relu', padding='same')(conv7)\n    \n    up8 = layers.Conv2DTranspose(128, 2, strides=(2, 2), padding='same')(conv7)\n    concat8 = layers.Concatenate()([up8, conv2])\n    conv8 = layers.Conv2D(128, 3, activation='relu', padding='same')(concat8)\n    conv8 = layers.Conv2D(128, 3, activation='relu', padding='same')(conv8)\n    \n    up9 = layers.Conv2DTranspose(64, 2, strides=(2, 2), padding='same')(conv8)\n    concat9 = layers.Concatenate()([up9, conv1])\n    conv9 = layers.Conv2D(64, 3, activation='relu', padding='same')(concat9)\n    conv9 = layers.Conv2D(64, 3, activation='relu', padding='same')(conv9)\n    \n    outputs = layers.Conv2D(1, 1, activation='sigmoid')(conv9)\n    \n    model = models.Model(inputs=[inputs], outputs=[outputs])\n    \n    return model\n\n# To Compile and train the model\ndef compile_and_train_model(model, X_train, Y_train, X_val, Y_val):\n    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n    history = model.fit(X_train, Y_train, validation_data=(X_val, Y_val), epochs=5, batch_size=8)\n    return history","metadata":{"execution":{"iopub.status.busy":"2024-09-10T07:06:52.260232Z","iopub.execute_input":"2024-09-10T07:06:52.260636Z","iopub.status.idle":"2024-09-10T07:06:52.280741Z","shell.execute_reply.started":"2024-09-10T07:06:52.260600Z","shell.execute_reply":"2024-09-10T07:06:52.279687Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\n# Disabling XLA to avoid XLA warnings\nos.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices=false'\n\ninput_shape = (128, 128, 1)\nattention_unet = attention_unet_model(input_shape)\n\n# Resizing data to match the input shape of the model\nX_train_resized = resize_images(X_train, (128, 128, 1))\nX_val_resized = resize_images(X_val, (128, 128, 1))\nX_test_resized = resize_images(X_test, (128, 128, 1))\nY_train_resized = resize_images(Y_train, (128, 128, 1))\nY_val_resized = resize_images(Y_val, (128, 128, 1))\nY_test_resized = resize_images(Y_test, (128, 128, 1))\n\nhistory_attention_unet = compile_and_train_model(attention_unet, X_train_resized, Y_train_resized, X_val_resized, Y_val_resized)","metadata":{"execution":{"iopub.status.busy":"2024-09-10T07:06:55.058234Z","iopub.execute_input":"2024-09-10T07:06:55.058639Z","iopub.status.idle":"2024-09-10T07:11:18.966009Z","shell.execute_reply.started":"2024-09-10T07:06:55.058602Z","shell.execute_reply":"2024-09-10T07:11:18.965148Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stdout","text":"Epoch 1/5\n\u001b[1m360/360\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 136ms/step - accuracy: 0.9601 - loss: 1.3220 - val_accuracy: 0.9862 - val_loss: 0.0804\nEpoch 2/5\n\u001b[1m360/360\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 131ms/step - accuracy: 0.9859 - loss: 0.0814 - val_accuracy: 0.9863 - val_loss: 0.0718\nEpoch 3/5\n\u001b[1m360/360\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 131ms/step - accuracy: 0.9865 - loss: 0.0706 - val_accuracy: 0.9863 - val_loss: 0.0691\nEpoch 4/5\n\u001b[1m360/360\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 130ms/step - accuracy: 0.9864 - loss: 0.0692 - val_accuracy: 0.9863 - val_loss: 0.0677\nEpoch 5/5\n\u001b[1m360/360\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 130ms/step - accuracy: 0.9870 - loss: 0.0671 - val_accuracy: 0.9863 - val_loss: 0.0678\n","output_type":"stream"}]},{"cell_type":"code","source":"def plot_learning_curves(history):\n    # Get training and validation metrics from the history object\n    acc = history.history['accuracy']\n    val_acc = history.history['val_accuracy']\n    loss = history.history['loss']\n    val_loss = history.history['val_loss']\n    \n    acc = [a * 100 for a in acc]\n    val_acc = [a * 100 for a in val_acc]\n    \n    epochs = range(1, len(acc) + 1)\n    \n    # Plotting Accuracy\n    plt.figure(figsize=(12, 8))  # Make the figure larger\n    plt.subplot(2, 1, 1)  # Create subplots (2 rows, 1 column, 1st plot)\n    plt.plot(epochs, acc, 'bo-', label='Training Accuracy')\n    plt.plot(epochs, val_acc, 'r*-', label='Validation Accuracy')\n    plt.title('Training and Validation Accuracy')\n    plt.xlabel('Epochs')\n    plt.ylabel('Accuracy (%)')\n    plt.legend()\n    plt.grid(True)\n    \n    # Plotting Loss\n    plt.subplot(2, 1, 2)\n    plt.plot(epochs, loss, 'bo-', label='Training Loss')\n    plt.plot(epochs, val_loss, 'r*-', label='Validation Loss')\n    plt.title('Training and Validation Loss')\n    plt.xlabel('Epochs')\n    plt.ylabel('Loss (Normalized)')\n    plt.legend()\n    plt.grid(True)\n    \n    plt.tight_layout()\n    plt.show()\n\nplot_learning_curves(history_attention_unet)","metadata":{"execution":{"iopub.status.busy":"2024-09-10T07:11:34.287967Z","iopub.execute_input":"2024-09-10T07:11:34.288368Z","iopub.status.idle":"2024-09-10T07:11:34.868244Z","shell.execute_reply.started":"2024-09-10T07:11:34.288331Z","shell.execute_reply":"2024-09-10T07:11:34.867318Z"},"trusted":true},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x800 with 2 Axes>","image/png":"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Evaluate the model on the test set\ndef evaluate_model(model, X_test, Y_test):\n    # Evaluate the model\n    test_loss, test_accuracy = model.evaluate(X_test, Y_test, verbose=1)\n    print(f\"Test Loss: {test_loss}\")\n    print(f\"Test Accuracy: {test_accuracy}\")\n    \n    return test_loss, test_accuracy\n\n# Evaluate and visualize predictions\ntest_loss, test_accuracy = evaluate_model(attention_unet, X_test_resized, Y_test_resized)","metadata":{"execution":{"iopub.status.busy":"2024-09-10T07:13:56.550654Z","iopub.execute_input":"2024-09-10T07:13:56.551073Z","iopub.status.idle":"2024-09-10T07:13:59.937480Z","shell.execute_reply.started":"2024-09-10T07:13:56.551034Z","shell.execute_reply":"2024-09-10T07:13:59.936647Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 100ms/step - accuracy: 0.9858 - loss: 0.0708\nTest Loss: 0.07177067548036575\nTest Accuracy: 0.9858872890472412\n","output_type":"stream"}]}]}