challenge-2-v4.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":7232999,"sourceType":"datasetVersion","datasetId":4188273},{"sourceId":7156698,"sourceType":"datasetVersion","datasetId":4133116}],"dockerImageVersionId":30627,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\n\n# Load the numpy array from the file\ndata = np.load('/kaggle/input/training/training_data.npy')\nvalid_periods = np.load('/kaggle/input/training/valid_periods.npy')\ndata.shape","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-12-19T00:55:42.366712Z","iopub.execute_input":"2023-12-19T00:55:42.367126Z","iopub.status.idle":"2023-12-19T00:55:42.710718Z","shell.execute_reply.started":"2023-12-19T00:55:42.367096Z","shell.execute_reply":"2023-12-19T00:55:42.709670Z"},"trusted":true},"execution_count":162,"outputs":[{"execution_count":162,"output_type":"execute_result","data":{"text/plain":"(48000, 2776)"},"metadata":{}}]},{"cell_type":"code","source":"new_data = []\n\nnum_valid_points = valid_periods[:, 1] - valid_periods[:, 0] + 1\n\nfor idx in range(len(data)):  # Use range instead of len\n    start_idx = valid_periods[idx][0]\n\n    while start_idx + 209 <= valid_periods[idx][1]:  # Adjust the condition\n        # subarray of length 209 to be added to new data\n        interval = data[idx, start_idx:start_idx + 209]\n        new_data.append(interval)\n\n        start_idx += 209\n\n# Convert the list to a numpy array\ndata = np.array(new_data)\n\n# Check the shape of the new data\nprint(data.shape)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T00:55:51.692215Z","iopub.execute_input":"2023-12-19T00:55:51.693127Z","iopub.status.idle":"2023-12-19T00:55:51.834252Z","shell.execute_reply.started":"2023-12-19T00:55:51.693090Z","shell.execute_reply":"2023-12-19T00:55:51.833185Z"},"trusted":true},"execution_count":164,"outputs":[{"name":"stdout","text":"(25156, 209)\n","output_type":"stream"}]},{"cell_type":"code","source":"# Calculate the number of valid points for each function\nnum_valid_points = valid_periods[:, 1] - valid_periods[:, 0] + 1\n\n# Filter out functions with less than 210 valid points\nfiltered_data = data[num_valid_points >= 210]\n\n# Check the shape of the filtered data\nprint(filtered_data.shape)\ndata = filtered_data","metadata":{"execution":{"iopub.status.busy":"2023-12-19T00:51:39.189654Z","iopub.execute_input":"2023-12-19T00:51:39.190488Z","iopub.status.idle":"2023-12-19T00:51:39.341894Z","shell.execute_reply.started":"2023-12-19T00:51:39.190448Z","shell.execute_reply":"2023-12-19T00:51:39.340924Z"},"trusted":true},"execution_count":153,"outputs":[{"name":"stdout","text":"(21970, 2776)\n","output_type":"stream"}]},{"cell_type":"code","source":"# Shuffle the datasets in unison\nperm = np.random.permutation(data.shape[0])\ndata_shuffled = data[perm]\nvalid_periods_shuffled = valid_periods[perm]\n\nK = 10000\n# Split into training and validation sets\nvalidation_data = data_shuffled[:K]\nvalidation_periods = valid_periods_shuffled[:K]\n\ntraining_data = data_shuffled[K:]\ntraining_periods = valid_periods_shuffled[K:]","metadata":{"execution":{"iopub.status.busy":"2023-12-19T00:55:56.841339Z","iopub.execute_input":"2023-12-19T00:55:56.842056Z","iopub.status.idle":"2023-12-19T00:55:56.867987Z","shell.execute_reply.started":"2023-12-19T00:55:56.842023Z","shell.execute_reply":"2023-12-19T00:55:56.867088Z"},"trusted":true},"execution_count":165,"outputs":[]},{"cell_type":"code","source":"def apply_valid_periods(data, valid_periods):\n    \"\"\"\n    Modify each function in 'data' based on the corresponding 'valid_periods'.\n\n    Parameters:\n    data (numpy.ndarray): Array of functions, shape (n_samples, n_features).\n    valid_periods (numpy.ndarray): Array of valid periods, shape (n_samples, 2).\n\n    Returns:\n    numpy.ndarray: Modified data array.\n    \"\"\"\n    modified_data = np.zeros_like(data)\n    n_samples, n_features = data.shape\n\n    for i in range(n_samples):\n        left, right = valid_periods[i]\n        # Assuming 'left' and 'right' are indices in the range [0, n_features-1]\n        # Adjust them if they are in a different format\n        modified_data[i, left:right+1] = data[i, left:right+1]\n\n    return modified_data\n\n# Apply the function to your datasets\nmodified_training_data = apply_valid_periods(training_data, training_periods)\nmodified_validation_data = apply_valid_periods(validation_data, validation_periods)\n#modified_test_data = apply_valid_periods(test_data, test_periods)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-19T00:55:57.104067Z","iopub.execute_input":"2023-12-19T00:55:57.104913Z","iopub.status.idle":"2023-12-19T00:55:57.205334Z","shell.execute_reply.started":"2023-12-19T00:55:57.104871Z","shell.execute_reply":"2023-12-19T00:55:57.204450Z"},"trusted":true},"execution_count":166,"outputs":[]},{"cell_type":"code","source":"def extract_fixed_intervals(data, interval_length=209):\n    \"\"\"\n    For each function in 'data', extract a random interval of 'interval_length'.\n    If a function has fewer than 'interval_length' points, pad the rest with zeros.\n\n    Parameters:\n    data (numpy.ndarray): Array of functions, shape (n_samples, n_features).\n    interval_length (int): Length of the interval to extract.\n\n    Returns:\n    numpy.ndarray: Data array with fixed-size intervals extracted.\n    \"\"\"\n    n_samples, n_features = data.shape\n    modified_data = np.zeros((n_samples, interval_length))\n\n    for i in range(n_samples):\n        nonzero_indices = np.nonzero(data[i])[0]\n\n        # Check if there are enough nonzero points\n        if nonzero_indices.size >= interval_length:\n            start_idx = np.random.choice(nonzero_indices[:-interval_length+1])\n            end_idx = start_idx + interval_length\n            modified_data[i] = data[i, start_idx:end_idx]\n        else:\n            # If not enough points, just copy what is available\n            modified_data[i, :nonzero_indices.size] = data[i, nonzero_indices]\n\n    return modified_data\n\n# Apply the function to your datasets\nfinal_training_data = extract_fixed_intervals(modified_training_data)\nfinal_validation_data = extract_fixed_intervals(modified_validation_data)\n#final_test_data = extract_fixed_intervals(modified_test_data)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-19T00:55:57.330652Z","iopub.execute_input":"2023-12-19T00:55:57.331008Z","iopub.status.idle":"2023-12-19T00:55:57.495542Z","shell.execute_reply.started":"2023-12-19T00:55:57.330981Z","shell.execute_reply":"2023-12-19T00:55:57.494702Z"},"trusted":true},"execution_count":167,"outputs":[]},{"cell_type":"code","source":"final_training_data.shape","metadata":{"execution":{"iopub.status.busy":"2023-12-19T00:55:57.587458Z","iopub.execute_input":"2023-12-19T00:55:57.588335Z","iopub.status.idle":"2023-12-19T00:55:57.594206Z","shell.execute_reply.started":"2023-12-19T00:55:57.588303Z","shell.execute_reply":"2023-12-19T00:55:57.593190Z"},"trusted":true},"execution_count":168,"outputs":[{"execution_count":168,"output_type":"execute_result","data":{"text/plain":"(15156, 209)"},"metadata":{}}]},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras.layers import Input, Dense, Dropout, Attention, LayerNormalization\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import EarlyStopping, TerminateOnNaN, LearningRateScheduler\n\n# Assuming final_training_data and final_validation_data are your datasets\n# Reshape data for Transformer input\nX_train = final_training_data[:, :-9]\ny_train = final_training_data[:, -9:]\n\nX_val = final_validation_data[:, :-9]\ny_val = final_validation_data[:, -9:]\n\n# Hyperparameters\nd_model = 128  # Dimensionality of the model\nnum_heads = 64   # Number of attention heads\nff_dim = 128    # Dimensionality of the feed-forward layer\n\n# Input layer\ninput_layer = Input(shape=(X_train.shape[1],))\n\n# Transformer Encoder\nx = Dense(d_model, activation='relu')(input_layer)\nx = Dropout(0.1)(x)\n\nx = Dense(ff_dim, activation='relu')(x)\nx = Dropout(0.1)(x)\n\n# Add layer normalization\nx = LayerNormalization()(x)\n\n# Output layer\noutput_layer = Dense(9)(x)\n\n# Create and compile the model\nmodel = Model(inputs=input_layer, outputs=output_layer)\noptimizer = Adam(lr=0.01)  # Adjust the initial learning rate\nmodel.compile(optimizer=optimizer, loss='mse')\n\n# Callbacks\nearly_stopping = EarlyStopping(monitor='val_mse', patience=20, restore_best_weights=True)\nterminate_on_nan = TerminateOnNaN()\n\n# Learning rate scheduler\ndef lr_scheduler(epoch, lr):\n    if epoch < 100:\n        return lr\n    else:\n        return lr * tf.math.exp(-0.00002)\n\nlearning_rate_scheduler = LearningRateScheduler(lr_scheduler)\n\n# Train the model\nmodel.fit(\n    X_train, \n    y_train, \n    validation_data=(X_val, y_val), \n    epochs=300, \n    batch_size=256,\n    callbacks=[early_stopping, terminate_on_nan, learning_rate_scheduler]\n)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-19T00:55:57.925883Z","iopub.execute_input":"2023-12-19T00:55:57.926401Z"},"trusted":true},"execution_count":null,"outputs":[{"name":"stdout","text":"Epoch 1/300\n60/60 [==============================] - 3s 24ms/step - loss: 2.8556e-04 - val_loss: 3.1792e-04 - lr: 0.0010\nEpoch 2/300\n60/60 [==============================] - 0s 5ms/step - loss: 7.5790e-05 - val_loss: 1.0402e-04 - lr: 0.0010\nEpoch 3/300\n60/60 [==============================] - 0s 5ms/step - loss: 9.3355e-05 - val_loss: 1.5461e-04 - lr: 0.0010\nEpoch 4/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.9234e-05 - val_loss: 2.3628e-04 - lr: 0.0010\nEpoch 5/300\n60/60 [==============================] - 0s 5ms/step - loss: 2.1776e-05 - val_loss: 1.9997e-04 - lr: 0.0010\nEpoch 6/300\n60/60 [==============================] - 0s 6ms/step - loss: 7.2600e-05 - val_loss: 2.0119e-04 - lr: 0.0010\nEpoch 7/300\n60/60 [==============================] - 0s 6ms/step - loss: 7.9390e-05 - val_loss: 1.7482e-04 - lr: 0.0010\nEpoch 8/300\n60/60 [==============================] - 0s 5ms/step - loss: 5.6394e-05 - val_loss: 1.9997e-04 - lr: 0.0010\nEpoch 9/300\n60/60 [==============================] - 0s 6ms/step - loss: 5.3550e-05 - val_loss: 1.9407e-04 - lr: 0.0010\nEpoch 10/300\n60/60 [==============================] - 0s 6ms/step - loss: 7.7201e-05 - val_loss: 1.5128e-04 - lr: 0.0010\nEpoch 11/300\n60/60 [==============================] - 0s 6ms/step - loss: 2.5330e-05 - val_loss: 1.4342e-04 - lr: 0.0010\nEpoch 12/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.6401e-05 - val_loss: 1.4506e-04 - lr: 0.0010\nEpoch 13/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.1483e-05 - val_loss: 8.2804e-05 - lr: 0.0010\nEpoch 14/300\n60/60 [==============================] - 0s 5ms/step - loss: 4.9958e-05 - val_loss: 1.3824e-04 - lr: 0.0010\nEpoch 15/300\n60/60 [==============================] - 0s 5ms/step - loss: 4.9298e-05 - val_loss: 1.5893e-04 - lr: 0.0010\nEpoch 16/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.9879e-05 - val_loss: 1.0638e-04 - lr: 0.0010\nEpoch 17/300\n60/60 [==============================] - 0s 6ms/step - loss: 2.0587e-05 - val_loss: 9.7217e-05 - lr: 0.0010\nEpoch 18/300\n60/60 [==============================] - 0s 5ms/step - loss: 5.0963e-06 - val_loss: 9.8783e-05 - lr: 0.0010\nEpoch 19/300\n60/60 [==============================] - 0s 5ms/step - loss: 2.7748e-06 - val_loss: 1.2141e-04 - lr: 0.0010\nEpoch 20/300\n60/60 [==============================] - 0s 5ms/step - loss: 5.9075e-06 - val_loss: 1.0289e-04 - lr: 0.0010\nEpoch 21/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.8115e-05 - val_loss: 9.5165e-05 - lr: 0.0010\nEpoch 22/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.4021e-05 - val_loss: 1.1643e-04 - lr: 0.0010\nEpoch 23/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.2969e-05 - val_loss: 1.3096e-04 - lr: 0.0010\nEpoch 24/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.2177e-05 - val_loss: 8.4398e-05 - lr: 0.0010\nEpoch 25/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.2733e-05 - val_loss: 1.3516e-04 - lr: 0.0010\nEpoch 26/300\n60/60 [==============================] - 0s 6ms/step - loss: 2.2746e-05 - val_loss: 1.1843e-04 - lr: 0.0010\nEpoch 27/300\n60/60 [==============================] - 0s 5ms/step - loss: 3.9273e-05 - val_loss: 1.4178e-04 - lr: 0.0010\nEpoch 28/300\n60/60 [==============================] - 0s 6ms/step - loss: 2.0375e-05 - val_loss: 7.8417e-05 - lr: 0.0010\nEpoch 29/300\n60/60 [==============================] - 0s 5ms/step - loss: 2.3340e-05 - val_loss: 8.5999e-05 - lr: 0.0010\nEpoch 30/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.7385e-05 - val_loss: 9.0202e-05 - lr: 0.0010\nEpoch 31/300\n60/60 [==============================] - 0s 6ms/step - loss: 8.3042e-06 - val_loss: 6.9771e-05 - lr: 0.0010\nEpoch 32/300\n60/60 [==============================] - 0s 6ms/step - loss: 9.6013e-06 - val_loss: 7.5209e-05 - lr: 0.0010\nEpoch 33/300\n60/60 [==============================] - 0s 5ms/step - loss: 2.4417e-05 - val_loss: 1.2140e-04 - lr: 0.0010\nEpoch 34/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.4950e-05 - val_loss: 9.2947e-05 - lr: 0.0010\nEpoch 35/300\n60/60 [==============================] - 0s 5ms/step - loss: 8.3393e-06 - val_loss: 7.8746e-05 - lr: 0.0010\nEpoch 36/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.4049e-05 - val_loss: 9.2529e-05 - lr: 0.0010\nEpoch 37/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.7341e-05 - val_loss: 9.2474e-05 - lr: 0.0010\nEpoch 38/300\n60/60 [==============================] - 0s 6ms/step - loss: 9.1029e-06 - val_loss: 1.1804e-04 - lr: 0.0010\nEpoch 39/300\n60/60 [==============================] - 0s 6ms/step - loss: 4.7040e-05 - val_loss: 1.0604e-04 - lr: 0.0010\nEpoch 40/300\n60/60 [==============================] - 0s 5ms/step - loss: 2.1071e-05 - val_loss: 9.5245e-05 - lr: 0.0010\nEpoch 41/300\n60/60 [==============================] - 0s 5ms/step - loss: 5.1741e-06 - val_loss: 9.8483e-05 - lr: 0.0010\nEpoch 42/300\n60/60 [==============================] - 0s 6ms/step - loss: 2.7940e-06 - val_loss: 1.0985e-04 - lr: 0.0010\nEpoch 43/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.5284e-05 - val_loss: 1.0231e-04 - lr: 0.0010\nEpoch 44/300\n60/60 [==============================] - 0s 6ms/step - loss: 4.0644e-06 - val_loss: 9.9031e-05 - lr: 0.0010\nEpoch 45/300\n60/60 [==============================] - 0s 6ms/step - loss: 2.0778e-05 - val_loss: 1.0429e-04 - lr: 0.0010\nEpoch 46/300\n60/60 [==============================] - 0s 6ms/step - loss: 4.3424e-05 - val_loss: 1.1559e-04 - lr: 0.0010\nEpoch 47/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.7902e-05 - val_loss: 1.0899e-04 - lr: 0.0010\nEpoch 48/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.1145e-06 - val_loss: 1.0066e-04 - lr: 0.0010\nEpoch 49/300\n60/60 [==============================] - 0s 6ms/step - loss: 7.4492e-06 - val_loss: 1.1591e-04 - lr: 0.0010\nEpoch 50/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.6510e-06 - val_loss: 1.1215e-04 - lr: 0.0010\nEpoch 51/300\n60/60 [==============================] - 0s 6ms/step - loss: 9.8358e-08 - val_loss: 1.1071e-04 - lr: 0.0010\nEpoch 52/300\n60/60 [==============================] - 0s 6ms/step - loss: 2.7471e-06 - val_loss: 1.1644e-04 - lr: 0.0010\nEpoch 53/300\n60/60 [==============================] - 0s 6ms/step - loss: 2.7313e-05 - val_loss: 9.5104e-05 - lr: 0.0010\nEpoch 54/300\n60/60 [==============================] - 0s 6ms/step - loss: 5.9548e-06 - val_loss: 9.7652e-05 - lr: 0.0010\nEpoch 55/300\n60/60 [==============================] - 0s 6ms/step - loss: 5.8226e-06 - val_loss: 8.8991e-05 - lr: 0.0010\nEpoch 56/300\n60/60 [==============================] - 0s 6ms/step - loss: 5.3292e-06 - val_loss: 1.2009e-04 - lr: 0.0010\nEpoch 57/300\n60/60 [==============================] - 0s 5ms/step - loss: 9.6538e-06 - val_loss: 9.0777e-05 - lr: 0.0010\nEpoch 58/300\n60/60 [==============================] - 0s 5ms/step - loss: 5.3051e-06 - val_loss: 9.4147e-05 - lr: 0.0010\nEpoch 59/300\n60/60 [==============================] - 0s 6ms/step - loss: 9.8538e-06 - val_loss: 9.8515e-05 - lr: 0.0010\nEpoch 60/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.2079e-06 - val_loss: 9.7558e-05 - lr: 0.0010\nEpoch 61/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.7544e-06 - val_loss: 9.2928e-05 - lr: 0.0010\nEpoch 62/300\n60/60 [==============================] - 0s 6ms/step - loss: 2.1474e-06 - val_loss: 9.6405e-05 - lr: 0.0010\nEpoch 63/300\n60/60 [==============================] - 0s 6ms/step - loss: 4.2444e-06 - val_loss: 9.2304e-05 - lr: 0.0010\nEpoch 64/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.7781e-06 - val_loss: 8.6672e-05 - lr: 0.0010\nEpoch 65/300\n60/60 [==============================] - 0s 6ms/step - loss: 9.4649e-06 - val_loss: 9.8581e-05 - lr: 0.0010\nEpoch 66/300\n60/60 [==============================] - 0s 5ms/step - loss: 4.9240e-06 - val_loss: 9.5029e-05 - lr: 0.0010\nEpoch 67/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.2595e-05 - val_loss: 9.9206e-05 - lr: 0.0010\nEpoch 68/300\n60/60 [==============================] - 0s 5ms/step - loss: 6.9086e-06 - val_loss: 9.7570e-05 - lr: 0.0010\nEpoch 69/300\n60/60 [==============================] - 0s 6ms/step - loss: 2.5706e-06 - val_loss: 9.5588e-05 - lr: 0.0010\nEpoch 70/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.7014e-06 - val_loss: 9.7358e-05 - lr: 0.0010\nEpoch 71/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.1427e-07 - val_loss: 9.5107e-05 - lr: 0.0010\nEpoch 72/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.1495e-06 - val_loss: 1.0168e-04 - lr: 0.0010\nEpoch 73/300\n60/60 [==============================] - 0s 5ms/step - loss: 3.2294e-06 - val_loss: 9.3361e-05 - lr: 0.0010\nEpoch 74/300\n60/60 [==============================] - 0s 6ms/step - loss: 7.0472e-07 - val_loss: 9.5157e-05 - lr: 0.0010\nEpoch 75/300\n60/60 [==============================] - 0s 5ms/step - loss: 3.2831e-06 - val_loss: 1.2299e-04 - lr: 0.0010\nEpoch 76/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.9867e-05 - val_loss: 9.4346e-05 - lr: 0.0010\nEpoch 77/300\n60/60 [==============================] - 0s 5ms/step - loss: 8.4172e-06 - val_loss: 1.0097e-04 - lr: 0.0010\nEpoch 78/300\n60/60 [==============================] - 0s 6ms/step - loss: 7.7775e-06 - val_loss: 8.9318e-05 - lr: 0.0010\nEpoch 79/300\n60/60 [==============================] - 0s 7ms/step - loss: 1.0575e-06 - val_loss: 9.4169e-05 - lr: 0.0010\nEpoch 80/300\n60/60 [==============================] - 0s 6ms/step - loss: 9.4759e-07 - val_loss: 9.0684e-05 - lr: 0.0010\nEpoch 81/300\n60/60 [==============================] - 0s 7ms/step - loss: 5.9347e-07 - val_loss: 8.9170e-05 - lr: 0.0010\nEpoch 82/300\n60/60 [==============================] - 0s 6ms/step - loss: 4.9392e-07 - val_loss: 9.3885e-05 - lr: 0.0010\nEpoch 83/300\n60/60 [==============================] - 0s 6ms/step - loss: 9.8560e-06 - val_loss: 8.8355e-05 - lr: 0.0010\nEpoch 84/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.5035e-05 - val_loss: 9.6619e-05 - lr: 0.0010\nEpoch 85/300\n60/60 [==============================] - 0s 5ms/step - loss: 4.2088e-06 - val_loss: 9.4433e-05 - lr: 0.0010\nEpoch 86/300\n60/60 [==============================] - 0s 6ms/step - loss: 9.0921e-07 - val_loss: 9.9900e-05 - lr: 0.0010\nEpoch 87/300\n60/60 [==============================] - 0s 6ms/step - loss: 6.4425e-07 - val_loss: 9.5152e-05 - lr: 0.0010\nEpoch 88/300\n60/60 [==============================] - 0s 5ms/step - loss: 5.3840e-07 - val_loss: 9.4893e-05 - lr: 0.0010\nEpoch 89/300\n60/60 [==============================] - 0s 5ms/step - loss: 6.3472e-08 - val_loss: 9.6721e-05 - lr: 0.0010\nEpoch 90/300\n60/60 [==============================] - 0s 6ms/step - loss: 4.7284e-07 - val_loss: 9.3210e-05 - lr: 0.0010\nEpoch 91/300\n60/60 [==============================] - 0s 5ms/step - loss: 3.6126e-07 - val_loss: 9.4671e-05 - lr: 0.0010\nEpoch 92/300\n60/60 [==============================] - 0s 5ms/step - loss: 2.6856e-06 - val_loss: 9.2093e-05 - lr: 0.0010\nEpoch 93/300\n60/60 [==============================] - 0s 5ms/step - loss: 5.4187e-06 - val_loss: 8.9089e-05 - lr: 0.0010\nEpoch 94/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.0657e-06 - val_loss: 9.3115e-05 - lr: 0.0010\nEpoch 95/300\n60/60 [==============================] - 0s 5ms/step - loss: 6.7137e-07 - val_loss: 8.6537e-05 - lr: 0.0010\nEpoch 96/300\n60/60 [==============================] - 0s 5ms/step - loss: 9.8186e-07 - val_loss: 9.5016e-05 - lr: 0.0010\nEpoch 97/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.6821e-06 - val_loss: 1.0567e-04 - lr: 0.0010\nEpoch 98/300\n60/60 [==============================] - 0s 5ms/step - loss: 7.0958e-07 - val_loss: 1.0648e-04 - lr: 0.0010\nEpoch 99/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.4037e-06 - val_loss: 9.4288e-05 - lr: 0.0010\nEpoch 100/300\n60/60 [==============================] - 0s 5ms/step - loss: 3.2517e-07 - val_loss: 9.3798e-05 - lr: 0.0010\nEpoch 101/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.8862e-07 - val_loss: 9.5538e-05 - lr: 9.9998e-04\nEpoch 102/300\n60/60 [==============================] - 0s 6ms/step - loss: 2.5690e-08 - val_loss: 9.3824e-05 - lr: 9.9996e-04\nEpoch 103/300\n60/60 [==============================] - 0s 5ms/step - loss: 9.2953e-08 - val_loss: 9.4227e-05 - lr: 9.9994e-04\nEpoch 104/300\n60/60 [==============================] - 0s 6ms/step - loss: 2.6798e-06 - val_loss: 1.0537e-04 - lr: 9.9992e-04\nEpoch 105/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.8018e-06 - val_loss: 1.0878e-04 - lr: 9.9990e-04\nEpoch 106/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.4951e-06 - val_loss: 8.9939e-05 - lr: 9.9988e-04\nEpoch 107/300\n60/60 [==============================] - 0s 6ms/step - loss: 4.4049e-07 - val_loss: 9.4893e-05 - lr: 9.9986e-04\nEpoch 108/300\n60/60 [==============================] - 0s 6ms/step - loss: 6.6570e-07 - val_loss: 9.8835e-05 - lr: 9.9984e-04\nEpoch 109/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.6829e-06 - val_loss: 1.0839e-04 - lr: 9.9982e-04\nEpoch 110/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.0902e-06 - val_loss: 8.9641e-05 - lr: 9.9980e-04\nEpoch 111/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.0001e-06 - val_loss: 9.4938e-05 - lr: 9.9978e-04\nEpoch 112/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.0091e-07 - val_loss: 9.3774e-05 - lr: 9.9976e-04\nEpoch 113/300\n60/60 [==============================] - 0s 6ms/step - loss: 6.7287e-08 - val_loss: 9.3431e-05 - lr: 9.9974e-04\nEpoch 114/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.0322e-07 - val_loss: 9.5169e-05 - lr: 9.9972e-04\nEpoch 115/300\n60/60 [==============================] - 0s 6ms/step - loss: 6.0085e-07 - val_loss: 9.1822e-05 - lr: 9.9970e-04\nEpoch 116/300\n60/60 [==============================] - 0s 6ms/step - loss: 7.8864e-07 - val_loss: 9.4725e-05 - lr: 9.9968e-04\nEpoch 117/300\n60/60 [==============================] - 0s 6ms/step - loss: 4.7347e-07 - val_loss: 9.7321e-05 - lr: 9.9966e-04\nEpoch 118/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.0733e-06 - val_loss: 9.0678e-05 - lr: 9.9964e-04\nEpoch 119/300\n60/60 [==============================] - 0s 5ms/step - loss: 5.6522e-07 - val_loss: 9.6912e-05 - lr: 9.9962e-04\nEpoch 120/300\n60/60 [==============================] - 0s 5ms/step - loss: 2.7810e-06 - val_loss: 9.6053e-05 - lr: 9.9960e-04\nEpoch 121/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.4532e-06 - val_loss: 9.5452e-05 - lr: 9.9958e-04\nEpoch 122/300\n60/60 [==============================] - 0s 6ms/step - loss: 7.0739e-07 - val_loss: 9.1292e-05 - lr: 9.9956e-04\nEpoch 123/300\n60/60 [==============================] - 0s 5ms/step - loss: 6.1358e-07 - val_loss: 8.8104e-05 - lr: 9.9954e-04\nEpoch 124/300\n60/60 [==============================] - 0s 6ms/step - loss: 2.1049e-07 - val_loss: 8.9451e-05 - lr: 9.9952e-04\nEpoch 125/300\n60/60 [==============================] - 0s 5ms/step - loss: 8.5132e-07 - val_loss: 1.1003e-04 - lr: 9.9950e-04\nEpoch 126/300\n60/60 [==============================] - 0s 5ms/step - loss: 8.9024e-07 - val_loss: 1.0389e-04 - lr: 9.9948e-04\nEpoch 127/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.4938e-06 - val_loss: 1.0667e-04 - lr: 9.9946e-04\nEpoch 128/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.2407e-06 - val_loss: 8.8745e-05 - lr: 9.9944e-04\nEpoch 129/300\n60/60 [==============================] - 0s 5ms/step - loss: 5.8616e-07 - val_loss: 9.9020e-05 - lr: 9.9942e-04\nEpoch 130/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.5327e-06 - val_loss: 8.8575e-05 - lr: 9.9940e-04\nEpoch 131/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.2229e-06 - val_loss: 9.3038e-05 - lr: 9.9938e-04\nEpoch 132/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.5120e-07 - val_loss: 9.1489e-05 - lr: 9.9936e-04\nEpoch 133/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.2843e-07 - val_loss: 9.2951e-05 - lr: 9.9934e-04\nEpoch 134/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.3585e-07 - val_loss: 9.0970e-05 - lr: 9.9932e-04\nEpoch 135/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.0892e-07 - val_loss: 9.3042e-05 - lr: 9.9930e-04\nEpoch 136/300\n60/60 [==============================] - 0s 6ms/step - loss: 2.4128e-07 - val_loss: 9.1819e-05 - lr: 9.9928e-04\nEpoch 137/300\n60/60 [==============================] - 0s 6ms/step - loss: 4.4687e-08 - val_loss: 9.2421e-05 - lr: 9.9926e-04\nEpoch 138/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.3259e-06 - val_loss: 1.1571e-04 - lr: 9.9924e-04\nEpoch 139/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.2171e-06 - val_loss: 8.8758e-05 - lr: 9.9922e-04\nEpoch 140/300\n60/60 [==============================] - 0s 6ms/step - loss: 5.7280e-07 - val_loss: 9.8062e-05 - lr: 9.9920e-04\nEpoch 141/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.5566e-06 - val_loss: 1.1208e-04 - lr: 9.9918e-04\nEpoch 142/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.7086e-06 - val_loss: 9.2850e-05 - lr: 9.9916e-04\nEpoch 143/300\n60/60 [==============================] - 0s 6ms/step - loss: 6.7032e-07 - val_loss: 9.5726e-05 - lr: 9.9914e-04\nEpoch 144/300\n60/60 [==============================] - 0s 6ms/step - loss: 4.2539e-07 - val_loss: 9.6935e-05 - lr: 9.9912e-04\nEpoch 145/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.8206e-07 - val_loss: 9.3913e-05 - lr: 9.9910e-04\nEpoch 146/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.5021e-07 - val_loss: 9.7214e-05 - lr: 9.9908e-04\nEpoch 147/300\n60/60 [==============================] - 0s 6ms/step - loss: 7.3504e-08 - val_loss: 9.5049e-05 - lr: 9.9906e-04\nEpoch 148/300\n60/60 [==============================] - 0s 6ms/step - loss: 9.1983e-08 - val_loss: 9.6240e-05 - lr: 9.9904e-04\nEpoch 149/300\n60/60 [==============================] - 0s 5ms/step - loss: 6.9810e-08 - val_loss: 9.5415e-05 - lr: 9.9902e-04\nEpoch 150/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.7368e-07 - val_loss: 1.0221e-04 - lr: 9.9900e-04\nEpoch 151/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.3319e-06 - val_loss: 1.0265e-04 - lr: 9.9898e-04\nEpoch 152/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.7230e-06 - val_loss: 9.0671e-05 - lr: 9.9896e-04\nEpoch 153/300\n60/60 [==============================] - 0s 6ms/step - loss: 3.1433e-06 - val_loss: 1.0343e-04 - lr: 9.9894e-04\nEpoch 154/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.1361e-06 - val_loss: 9.2747e-05 - lr: 9.9892e-04\nEpoch 155/300\n60/60 [==============================] - 0s 6ms/step - loss: 7.9232e-07 - val_loss: 9.3745e-05 - lr: 9.9890e-04\nEpoch 156/300\n60/60 [==============================] - 0s 6ms/step - loss: 8.6365e-07 - val_loss: 9.5912e-05 - lr: 9.9888e-04\nEpoch 157/300\n60/60 [==============================] - 0s 6ms/step - loss: 2.7943e-06 - val_loss: 9.4086e-05 - lr: 9.9886e-04\nEpoch 158/300\n60/60 [==============================] - 0s 6ms/step - loss: 6.6469e-07 - val_loss: 9.2424e-05 - lr: 9.9884e-04\nEpoch 159/300\n60/60 [==============================] - 0s 6ms/step - loss: 1.8830e-07 - val_loss: 9.4280e-05 - lr: 9.9882e-04\nEpoch 160/300\n60/60 [==============================] - 0s 5ms/step - loss: 6.6131e-08 - val_loss: 9.8646e-05 - lr: 9.9880e-04\nEpoch 161/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.4781e-07 - val_loss: 9.3349e-05 - lr: 9.9879e-04\nEpoch 162/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.6569e-07 - val_loss: 9.6231e-05 - lr: 9.9877e-04\nEpoch 163/300\n60/60 [==============================] - 0s 5ms/step - loss: 1.4139e-07 - val_loss: 9.1067e-05 - lr: 9.9875e-04\nEpoch 164/300\n42/60 [====================>.........] - ETA: 0s - loss: 5.1137e-06","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.metrics import mean_squared_error\n\n# Reshape validation data (if required)\nX_val_reshaped = X_val.reshape((X_val.shape[0], X_val.shape[1], 1))\n\n# Predict\npredictions = model.predict(X_val_reshaped)\n\n# Calculate MSE\nmse = mean_squared_error(y_val, predictions)\nprint(f\"Mean Squared Error: {mse}\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.save(f'{mse}.h5')\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}