{"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"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n#import os\n#for dirname, _, filenames in os.walk('/kaggle/input'):\n #   for filename in filenames:\n  #      print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-10-10T05:43:53.602773Z","iopub.execute_input":"2023-10-10T05:43:53.603874Z","iopub.status.idle":"2023-10-10T05:43:53.611769Z","shell.execute_reply.started":"2023-10-10T05:43:53.603824Z","shell.execute_reply":"2023-10-10T05:43:53.61046Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"import os\nimport librosa\nimport librosa.display\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import normalize\nimport warnings\nwarnings.filterwarnings('ignore')\nfrom sklearn.model_selection import train_test_split\nimport tensorflow\nfrom tensorflow.keras.layers import LSTM, Dense\nimport pandas as pd ","metadata":{"execution":{"iopub.status.busy":"2023-10-10T05:43:53.613501Z","iopub.execute_input":"2023-10-10T05:43:53.614137Z","iopub.status.idle":"2023-10-10T05:43:53.630784Z","shell.execute_reply.started":"2023-10-10T05:43:53.614106Z","shell.execute_reply":"2023-10-10T05:43:53.629854Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"os.chdir('/kaggle/input/rfcx-species-audio-detection')\ndf = pd.read_csv('train_tp.csv')","metadata":{"execution":{"iopub.status.busy":"2023-10-10T05:43:53.63243Z","iopub.execute_input":"2023-10-10T05:43:53.633511Z","iopub.status.idle":"2023-10-10T05:43:53.668531Z","shell.execute_reply.started":"2023-10-10T05:43:53.63347Z","shell.execute_reply":"2023-10-10T05:43:53.667762Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2023-10-10T05:43:53.698312Z","iopub.execute_input":"2023-10-10T05:43:53.699393Z","iopub.status.idle":"2023-10-10T05:43:53.718105Z","shell.execute_reply.started":"2023-10-10T05:43:53.699347Z","shell.execute_reply":"2023-10-10T05:43:53.716981Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"  recording_id  species_id  songtype_id    t_min     f_min    t_max     f_max\n0    003bec244          14            1  44.5440  2531.250  45.1307   5531.25\n1    006ab765f          23            1  39.9615  7235.160  46.0452  11283.40\n2    007f87ba2          12            1  39.1360   562.500  42.2720   3281.25\n3    0099c367b          17            4  51.4206  1464.260  55.1996   4565.04\n4    009b760e6          10            1  50.0854   947.461  52.5293  10852.70","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>recording_id</th>\n      <th>species_id</th>\n      <th>songtype_id</th>\n      <th>t_min</th>\n      <th>f_min</th>\n      <th>t_max</th>\n      <th>f_max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>003bec244</td>\n      <td>14</td>\n      <td>1</td>\n      <td>44.5440</td>\n      <td>2531.250</td>\n      <td>45.1307</td>\n      <td>5531.25</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>006ab765f</td>\n      <td>23</td>\n      <td>1</td>\n      <td>39.9615</td>\n      <td>7235.160</td>\n      <td>46.0452</td>\n      <td>11283.40</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>007f87ba2</td>\n      <td>12</td>\n      <td>1</td>\n      <td>39.1360</td>\n      <td>562.500</td>\n      <td>42.2720</td>\n      <td>3281.25</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0099c367b</td>\n      <td>17</td>\n      <td>4</td>\n      <td>51.4206</td>\n      <td>1464.260</td>\n      <td>55.1996</td>\n      <td>4565.04</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>009b760e6</td>\n      <td>10</td>\n      <td>1</td>\n      <td>50.0854</td>\n      <td>947.461</td>\n      <td>52.5293</td>\n      <td>10852.70</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Librosa library is used to laod and Display an audio file ","metadata":{}},{"cell_type":"code","source":"sample_num=3 #pick a file to display\nfilename=df.recording_id[sample_num]+str('.flac') #get the filename\n#define the beginning time of the signal\ntstart = df.t_min[sample_num] \ntend = df.t_max[sample_num] #define the end time of the signal\ny,sr=librosa.load('train/'+str(filename))\nlibrosa.display.waveshow(y,sr=sr, x_axis='time', color='purple',offset=0.0)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T05:43:53.719639Z","iopub.execute_input":"2023-10-10T05:43:53.720169Z","iopub.status.idle":"2023-10-10T05:43:54.566435Z","shell.execute_reply.started":"2023-10-10T05:43:53.720136Z","shell.execute_reply":"2023-10-10T05:43:54.565333Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"<librosa.display.AdaptiveWaveplot at 0x799986f27e80>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# Features for Modeling","metadata":{}},{"cell_type":"code","source":"hop_length = 512 #the default spacing between frames\nn_fft = 255 #number of samples \n#cut the sample to the relevant times\ny_cut=y[int(round(tstart*sr)):int(round(tend*sr))]\nMFCCs = librosa.feature.mfcc(y=y_cut, n_fft=n_fft,hop_length=hop_length,n_mfcc=128)\nfig, ax = plt.subplots(figsize=(20,7))\nlibrosa.display.specshow(MFCCs,sr=sr, cmap='cool',hop_length=hop_length)\nax.set_xlabel('Time', fontsize=15)\nax.set_title('MFCC', size=20)\nplt.colorbar()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-10T05:43:54.567993Z","iopub.execute_input":"2023-10-10T05:43:54.569046Z","iopub.status.idle":"2023-10-10T05:43:54.887649Z","shell.execute_reply.started":"2023-10-10T05:43:54.569004Z","shell.execute_reply":"2023-10-10T05:43:54.88655Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x700 with 2 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":{}}]},{"cell_type":"markdown","source":"## Extracting Features & Labels for all the files and store in a numpy array","metadata":{}},{"cell_type":"code","source":"import numpy as np\n\ndef padding(data, target_length, padding_value=0):\n    \"\"\"\n    Pad the input data to the target length.\n    \n    Parameters:\n    data (numpy.ndarray): Input data.\n    target_length (int): Target length to pad the data to.\n    padding_value (int, optional): Value to use for padding. Defaults to 0.\n    \n    Returns:\n    numpy.ndarray: Padded data.\n    \"\"\"\n    if len(data) >= target_length:\n        return data[:target_length]\n    else:\n        padding = np.full((target_length - len(data),), padding_value)\n        return np.concatenate((data, padding))\n\n\ndef get_features(df_in):\n    features = []  # list to save features\n    labels = []  # list to save labels\n    max_length = 0  # initialize max length\n\n    for index in range(len(df_in)):\n        # get the filename\n        filename = df_in.iloc[index]['recording_id'] + '.flac'\n        # cut to start of signal\n        tstart = df_in.iloc[index]['t_min']\n        # cut to end of signal\n        tend = df_in.iloc[index]['t_max']\n        # save labels\n        species_id = df_in.iloc[index]['species_id']\n        # load the file\n        y, sr = librosa.load('train/' + filename, sr=28000)\n        # cut the file from tstart to tend\n        y_cut = y[int(round(tstart * sr)):int(round(tend * sr))]\n        data = librosa.feature.mfcc(y=y_cut, n_mfcc=128)\n\n        # Update max length\n        max_length = max(max_length, data.shape[1])\n\n        features.append(data)\n        labels.append(species_id)\n\n    # Pad features to have the same length\n    padded_features = [pad(data, max_length) for data in features]\n\n    return np.array(padded_features), labels\n\nX, y = get_features(df)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T06:01:43.718926Z","iopub.execute_input":"2023-10-10T06:01:43.7193Z","iopub.status.idle":"2023-10-10T06:06:33.996573Z","shell.execute_reply.started":"2023-10-10T06:01:43.719272Z","shell.execute_reply":"2023-10-10T06:06:33.995741Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"markdown","source":"## Normalize the data and cast into numpy array","metadata":{}},{"cell_type":"code","source":"X = np.array((X-np.min(X))/(np.max(X)-np.min(X)))\nX = X/np.std(X)\ny = np.array(y)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T06:42:04.276509Z","iopub.execute_input":"2023-10-10T06:42:04.277684Z","iopub.status.idle":"2023-10-10T06:42:05.255561Z","shell.execute_reply.started":"2023-10-10T06:42:04.277646Z","shell.execute_reply":"2023-10-10T06:42:05.254465Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"markdown","source":"## Extracting training, test and validation datasets","metadata":{}},{"cell_type":"code","source":"#Split twice to get the validation set\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=123, stratify=y)\nX_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=123)\n#Print the shapes\nX_train.shape, X_test.shape, X_val.shape, len(y_train), len(y_test), len(y_val)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T06:42:09.050753Z","iopub.execute_input":"2023-10-10T06:42:09.051103Z","iopub.status.idle":"2023-10-10T06:42:09.214947Z","shell.execute_reply.started":"2023-10-10T06:42:09.051076Z","shell.execute_reply":"2023-10-10T06:42:09.21371Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"((684, 128, 434), (304, 128, 434), (228, 128, 434), 684, 304, 228)"},"metadata":{}}]},{"cell_type":"markdown","source":"## RNN model created ","metadata":{}},{"cell_type":"code","source":"import numpy as np\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import LSTM, Dense, Dropout\nfrom tensorflow.keras.utils import to_categorical\n\n# Assuming num_classes is the number of classes in your dataset\nnum_classes = 24\n\n# One-hot encode the labels\ny_train_one_hot = to_categorical(y_train, num_classes=num_classes)\ny_val_one_hot = to_categorical(y_val, num_classes=num_classes)\n\n# Define your model\nmodel = Sequential()\ninput_shape = (128, 434)  # Assuming 1000 is the time steps\nmodel.add(LSTM(128, input_shape=input_shape))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dense(64, activation='relu'))\nmodel.add(Dropout(0.4))\nmodel.add(Dense(num_classes, activation='softmax'))  # Adjust to num_classes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-10-10T06:56:26.489386Z","iopub.execute_input":"2023-10-10T06:56:26.489737Z","iopub.status.idle":"2023-10-10T06:56:26.791136Z","shell.execute_reply.started":"2023-10-10T06:56:26.489708Z","shell.execute_reply":"2023-10-10T06:56:26.789853Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"markdown","source":"## Compile the model","metadata":{}},{"cell_type":"code","source":"# Compile the model\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2023-10-10T06:56:39.827854Z","iopub.execute_input":"2023-10-10T06:56:39.828218Z","iopub.status.idle":"2023-10-10T06:56:39.839466Z","shell.execute_reply.started":"2023-10-10T06:56:39.828192Z","shell.execute_reply":"2023-10-10T06:56:39.838475Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"markdown","source":"## Fit the model ","metadata":{}},{"cell_type":"code","source":"# Train the model\nhistory = model.fit(X_train, y_train_one_hot, epochs=50, batch_size=72,\n                    validation_data=(X_val, y_val_one_hot), shuffle=False)","metadata":{"execution":{"iopub.status.busy":"2023-10-10T06:57:01.528328Z","iopub.execute_input":"2023-10-10T06:57:01.528688Z","iopub.status.idle":"2023-10-10T06:59:25.831196Z","shell.execute_reply.started":"2023-10-10T06:57:01.528653Z","shell.execute_reply":"2023-10-10T06:59:25.829949Z"},"trusted":true},"execution_count":41,"outputs":[{"name":"stdout","text":"Epoch 1/50\n10/10 [==============================] - 5s 277ms/step - loss: 3.2075 - accuracy: 0.0570 - val_loss: 3.1657 - val_accuracy: 0.1053\nEpoch 2/50\n10/10 [==============================] - 2s 223ms/step - loss: 3.1979 - accuracy: 0.0556 - val_loss: 3.1723 - val_accuracy: 0.1053\nEpoch 3/50\n10/10 [==============================] - 2s 248ms/step - loss: 3.1823 - accuracy: 0.0424 - val_loss: 3.1765 - val_accuracy: 0.1053\nEpoch 4/50\n10/10 [==============================] - 2s 218ms/step - loss: 3.1668 - accuracy: 0.0716 - val_loss: 3.1803 - val_accuracy: 0.1053\nEpoch 5/50\n10/10 [==============================] - 2s 220ms/step - loss: 3.1711 - accuracy: 0.0512 - val_loss: 3.1822 - val_accuracy: 0.1053\nEpoch 6/50\n10/10 [==============================] - 2s 219ms/step - loss: 3.1718 - accuracy: 0.0716 - val_loss: 3.1771 - val_accuracy: 0.1053\nEpoch 7/50\n10/10 [==============================] - 2s 221ms/step - loss: 3.1851 - accuracy: 0.0541 - val_loss: 3.1743 - val_accuracy: 0.1053\nEpoch 8/50\n10/10 [==============================] - 2s 217ms/step - loss: 3.1676 - accuracy: 0.0643 - val_loss: 3.1700 - val_accuracy: 0.1053\nEpoch 9/50\n10/10 [==============================] - 2s 220ms/step - loss: 3.1739 - accuracy: 0.0687 - val_loss: 3.1691 - val_accuracy: 0.1053\nEpoch 10/50\n10/10 [==============================] - 2s 217ms/step - loss: 3.1666 - accuracy: 0.0716 - val_loss: 3.1747 - val_accuracy: 0.1053\nEpoch 11/50\n10/10 [==============================] - 2s 221ms/step - loss: 3.1663 - accuracy: 0.0643 - val_loss: 3.1751 - val_accuracy: 0.1053\nEpoch 12/50\n10/10 [==============================] - 2s 221ms/step - loss: 3.1675 - accuracy: 0.0629 - val_loss: 3.1751 - val_accuracy: 0.1053\nEpoch 13/50\n10/10 [==============================] - 2s 220ms/step - loss: 3.1691 - accuracy: 0.0702 - val_loss: 3.1724 - val_accuracy: 0.1053\nEpoch 14/50\n10/10 [==============================] - 2s 219ms/step - loss: 3.1598 - accuracy: 0.0833 - val_loss: 3.1699 - val_accuracy: 0.1053\nEpoch 15/50\n10/10 [==============================] - 2s 218ms/step - loss: 3.1614 - accuracy: 0.0746 - val_loss: 3.1730 - val_accuracy: 0.1053\nEpoch 16/50\n10/10 [==============================] - 2s 224ms/step - loss: 3.1620 - accuracy: 0.0702 - val_loss: 3.1746 - val_accuracy: 0.1053\nEpoch 17/50\n10/10 [==============================] - 2s 217ms/step - loss: 3.1695 - accuracy: 0.0789 - val_loss: 3.1764 - val_accuracy: 0.1053\nEpoch 18/50\n10/10 [==============================] - 2s 250ms/step - loss: 3.1648 - accuracy: 0.0673 - val_loss: 3.1753 - val_accuracy: 0.1053\nEpoch 19/50\n10/10 [==============================] - 2s 223ms/step - loss: 3.1629 - accuracy: 0.0687 - val_loss: 3.1744 - val_accuracy: 0.1053\nEpoch 20/50\n10/10 [==============================] - 2s 224ms/step - loss: 3.1622 - accuracy: 0.0731 - val_loss: 3.1751 - val_accuracy: 0.1053\nEpoch 21/50\n10/10 [==============================] - 2s 220ms/step - loss: 3.1607 - accuracy: 0.0599 - val_loss: 3.1772 - val_accuracy: 0.1053\nEpoch 22/50\n10/10 [==============================] - 2s 219ms/step - loss: 3.1620 - accuracy: 0.0629 - val_loss: 3.1799 - val_accuracy: 0.1053\nEpoch 23/50\n10/10 [==============================] - 2s 222ms/step - loss: 3.1574 - accuracy: 0.0673 - val_loss: 3.1813 - val_accuracy: 0.1053\nEpoch 24/50\n10/10 [==============================] - 2s 220ms/step - loss: 3.1626 - accuracy: 0.0804 - val_loss: 3.1809 - val_accuracy: 0.1053\nEpoch 25/50\n10/10 [==============================] - 2s 226ms/step - loss: 3.1610 - accuracy: 0.0673 - val_loss: 3.1809 - val_accuracy: 0.1053\nEpoch 26/50\n10/10 [==============================] - 2s 218ms/step - loss: 3.1697 - accuracy: 0.0658 - val_loss: 3.1812 - val_accuracy: 0.1053\nEpoch 27/50\n10/10 [==============================] - 2s 219ms/step - loss: 3.1603 - accuracy: 0.0658 - val_loss: 3.1796 - val_accuracy: 0.1053\nEpoch 28/50\n10/10 [==============================] - 2s 222ms/step - loss: 3.1553 - accuracy: 0.0775 - val_loss: 3.1805 - val_accuracy: 0.1053\nEpoch 29/50\n10/10 [==============================] - 2s 222ms/step - loss: 3.1594 - accuracy: 0.0702 - val_loss: 3.1815 - val_accuracy: 0.1053\nEpoch 30/50\n10/10 [==============================] - 2s 223ms/step - loss: 3.1559 - accuracy: 0.0731 - val_loss: 3.1816 - val_accuracy: 0.1053\nEpoch 31/50\n10/10 [==============================] - 2s 219ms/step - loss: 3.1616 - accuracy: 0.0760 - val_loss: 3.1810 - val_accuracy: 0.1053\nEpoch 32/50\n10/10 [==============================] - 2s 253ms/step - loss: 3.1619 - accuracy: 0.0760 - val_loss: 3.1801 - val_accuracy: 0.1053\nEpoch 33/50\n10/10 [==============================] - 2s 220ms/step - loss: 3.1606 - accuracy: 0.0731 - val_loss: 3.1778 - val_accuracy: 0.1053\nEpoch 34/50\n10/10 [==============================] - 2s 224ms/step - loss: 3.1673 - accuracy: 0.0746 - val_loss: 3.1742 - val_accuracy: 0.1053\nEpoch 35/50\n10/10 [==============================] - 2s 221ms/step - loss: 3.1583 - accuracy: 0.0746 - val_loss: 3.1737 - val_accuracy: 0.1053\nEpoch 36/50\n10/10 [==============================] - 2s 222ms/step - loss: 3.1618 - accuracy: 0.0760 - val_loss: 3.1740 - val_accuracy: 0.1053\nEpoch 37/50\n10/10 [==============================] - 2s 219ms/step - loss: 3.1576 - accuracy: 0.0746 - val_loss: 3.1750 - val_accuracy: 0.1053\nEpoch 38/50\n10/10 [==============================] - 2s 223ms/step - loss: 3.1509 - accuracy: 0.0746 - val_loss: 3.1757 - val_accuracy: 0.1053\nEpoch 39/50\n10/10 [==============================] - 2s 224ms/step - loss: 3.1568 - accuracy: 0.0760 - val_loss: 3.1772 - val_accuracy: 0.1053\nEpoch 40/50\n10/10 [==============================] - 2s 219ms/step - loss: 3.1635 - accuracy: 0.0746 - val_loss: 3.1769 - val_accuracy: 0.1053\nEpoch 41/50\n10/10 [==============================] - 2s 221ms/step - loss: 3.1563 - accuracy: 0.0746 - val_loss: 3.1747 - val_accuracy: 0.1053\nEpoch 42/50\n10/10 [==============================] - 2s 220ms/step - loss: 3.1525 - accuracy: 0.0702 - val_loss: 3.1773 - val_accuracy: 0.1053\nEpoch 43/50\n10/10 [==============================] - 2s 221ms/step - loss: 3.1651 - accuracy: 0.0775 - val_loss: 3.1779 - val_accuracy: 0.1053\nEpoch 44/50\n10/10 [==============================] - 2s 218ms/step - loss: 3.1621 - accuracy: 0.0731 - val_loss: 3.1779 - val_accuracy: 0.1053\nEpoch 45/50\n10/10 [==============================] - 2s 219ms/step - loss: 3.1588 - accuracy: 0.0731 - val_loss: 3.1773 - val_accuracy: 0.1053\nEpoch 46/50\n10/10 [==============================] - 2s 233ms/step - loss: 3.1610 - accuracy: 0.0731 - val_loss: 3.1758 - val_accuracy: 0.1053\nEpoch 47/50\n10/10 [==============================] - 2s 236ms/step - loss: 3.1556 - accuracy: 0.0760 - val_loss: 3.1762 - val_accuracy: 0.1053\nEpoch 48/50\n10/10 [==============================] - 2s 221ms/step - loss: 3.1535 - accuracy: 0.0775 - val_loss: 3.1771 - val_accuracy: 0.1053\nEpoch 49/50\n10/10 [==============================] - 2s 221ms/step - loss: 3.1626 - accuracy: 0.0716 - val_loss: 3.1740 - val_accuracy: 0.1053\nEpoch 50/50\n10/10 [==============================] - 2s 216ms/step - loss: 3.1574 - accuracy: 0.0716 - val_loss: 3.1790 - val_accuracy: 0.1053\n","output_type":"stream"}]},{"cell_type":"code","source":"input_shape=(128,434)\nmodel = tensorflow.keras.Sequential()\nmodel.add(LSTM(num_classes,input_shape=input_shape))\nmodel.add(Dense(24, activation='softmax'))\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2023-10-10T07:03:58.844972Z","iopub.execute_input":"2023-10-10T07:03:58.845372Z","iopub.status.idle":"2023-10-10T07:03:59.165376Z","shell.execute_reply.started":"2023-10-10T07:03:58.845341Z","shell.execute_reply":"2023-10-10T07:03:59.164202Z"},"trusted":true},"execution_count":46,"outputs":[{"name":"stdout","text":"Model: \"sequential_9\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n lstm_8 (LSTM)               (None, 24)                44064     \n                                                                 \n dense_23 (Dense)            (None, 24)                600       \n                                                                 \n=================================================================\nTotal params: 44,664\nTrainable params: 44,664\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Evaluate the model train and validation set","metadata":{}},{"cell_type":"code","source":"#Adapted from Deep Learning with Python by Francois Chollet, 2018\nhistory_dict=history.history\nloss_values=history_dict['loss']\nacc_values=history_dict['accuracy']\nval_loss_values = history_dict['val_loss']\nval_acc_values=history_dict['val_accuracy']\nepochs=range(1,51)\nfig,(ax1,ax2)=plt.subplots(1,2,figsize=(15,5))\nax1.plot(epochs,loss_values,'co',label='Training Loss')\nax1.plot(epochs,val_loss_values,'m', label='Validation Loss')\nax1.set_title('Training and validation loss')\nax1.set_xlabel('Epochs')\nax1.set_ylabel('Loss')\nax1.legend()\nax2.plot(epochs,acc_values,'co', label='Training accuracy')\nax2.plot(epochs,val_acc_values,'m',label='Validation accuracy')\nax2.set_title('Training and validation accuracy')\nax2.set_xlabel('Epochs')\nax2.set_ylabel('Accuracy')\nax2.legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-10T07:04:03.191463Z","iopub.execute_input":"2023-10-10T07:04:03.191817Z","iopub.status.idle":"2023-10-10T07:04:03.65692Z","shell.execute_reply.started":"2023-10-10T07:04:03.191788Z","shell.execute_reply":"2023-10-10T07:04:03.655823Z"},"trusted":true},"execution_count":47,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x500 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"## Checking how well the model predicts using a confusion matrix","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\n# Evaluate the model\ntrain_loss, train_acc = model.evaluate(X_train, y_train)\ntest_loss, test_acc = model.evaluate(X_test, y_test)\ny_pred = model.predict(X_test)\n\n# Print the confusion matrix\nconfusion_matrix = tf.math.confusion_matrix(y_test, np.argmax(y_pred, axis=1))\nprint('Confusion_matrix: ', confusion_matrix)\n","metadata":{"execution":{"iopub.status.busy":"2023-10-10T07:05:20.814047Z","iopub.execute_input":"2023-10-10T07:05:20.814538Z","iopub.status.idle":"2023-10-10T07:05:22.456053Z","shell.execute_reply.started":"2023-10-10T07:05:20.814508Z","shell.execute_reply":"2023-10-10T07:05:22.455018Z"},"trusted":true},"execution_count":50,"outputs":[{"name":"stdout","text":"22/22 [==============================] - 0s 20ms/step - loss: 3.1535 - accuracy: 0.0746\n10/10 [==============================] - 0s 18ms/step - loss: 3.1617 - accuracy: 0.0822\n10/10 [==============================] - 0s 19ms/step\nConfusion_matrix:  tf.Tensor(\n[[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 12]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 13]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  9]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 13]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 13]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 12]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 12]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 13]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 12]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  8]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 13]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 13]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 12]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 13]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 13]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 12]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 12]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 13]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 11]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 10]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 12]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 11]\n [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 25]], shape=(24, 24), dtype=int32)\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}