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form.onsubmit = function(e) { e.preventDefault(); var query = document.getElementById('search-input').value; window.location.href = '/search.html?query=' + query; return False } </script> </div> <div class='k-main-inner' id='k-main-id'> <div class='k-location-slug'> ► <a href='/examples/'>Code examples</a> / <a href='/examples/audio/'>Audio Data</a> / Audio Classification with the STFTSpectrogram layer </div> <div class='k-content'> <h1 id="audio-classification-with-the-stftspectrogram-layer">Audio Classification with the STFTSpectrogram layer</h1> Author: <a href="https://mostafa-amin.com">Mostafa M. Amin</a> Date created: 2024/10/04 Last modified: 2024/10/04 Description: Introducing the <code>STFTSpectrogram</code> layer to extract spectrograms for audio classification. <div class='example_version_banner keras_2'>ⓘ This example uses Keras 2</div> <img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> <a href="https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/audio/ipynb/stft.ipynb">View in Colab</a> •<img class="k-inline-icon" src="https://github.com/favicon.ico"/> <a href="https://github.com/keras-team/keras-io/blob/master/examples/audio/stft.py">GitHub source</a> <hr /> <h2 id="introduction">Introduction</h2> Preprocessing audio as spectrograms is an essential step in the vast majority of audio-based applications. Spectrograms represent the frequency content of a signal over time, are widely used for this purpose. In this tutorial, we'll demonstrate how to use the <code>STFTSpectrogram</code> layer in Keras to convert raw audio waveforms into spectrograms within the model. We'll then feed these spectrograms into an LSTM network followed by Dense layers to perform audio classification on the Speech Commands dataset. We will: <ul> <li>Load the ESC-10 dataset.</li> <li>Preprocess the raw audio waveforms and generate spectrograms using <code>STFTSpectrogram</code>.</li> <li>Build two models, one using spectrograms as 1D signals and the other is using as images (2D signals) with a pretrained image model.</li> <li>Train and evaluate the models.</li> </ul> <hr /> <h2 id="setup">Setup</h2> <h3 id="importing-the-necessary-libraries">Importing the necessary libraries</h3> <div class="codehilite"><pre><code>import os os.environ["KERAS_BACKEND"] = "jax" </code></pre></div> <div class="codehilite"><pre><code>import keras import matplotlib.pyplot as plt import numpy as np import pandas as pd import scipy.io.wavfile from keras import layers from scipy.signal import resample keras.utils.set_random_seed(41) </code></pre></div> <h3 id="define-some-variables">Define some variables</h3> <div class="codehilite"><pre><code>BASE_DATA_DIR = "./datasets/esc-50_extracted/ESC-50-master/" BATCH_SIZE = 16 NUM_CLASSES = 10 EPOCHS = 200 SAMPLE_RATE = 16000 </code></pre></div> <hr /> <h2 id="download-and-preprocess-the-esc10-dataset">Download and Preprocess the ESC-10 Dataset</h2> We'll use the Dataset for Environmental Sound Classification dataset (ESC-10). This dataset consists of five-second .wav files of environmental sounds. <h3 id="download-and-extract-the-dataset">Download and Extract the dataset</h3> <div class="codehilite"><pre><code>keras.utils.get_file( "esc-50.zip", "https://github.com/karoldvl/ESC-50/archive/master.zip", cache_dir="./", cache_subdir="datasets", extract=True, ) </code></pre></div> <div class="codehilite"><pre><code>'./datasets/esc-50_extracted' </code></pre></div> <h3 id="read-the-csv-file">Read the CSV file</h3> <div class="codehilite"><pre><code>pd_data = pd.read_csv(os.path.join(BASE_DATA_DIR, "meta", "esc50.csv")) # filter ESC-50 to ESC-10 and reassign the targets pd_data = pd_data[pd_data["esc10"]] targets = sorted(pd_data["target"].unique().tolist()) assert len(targets) == NUM_CLASSES old_target_to_new_target = {old: new for new, old in enumerate(targets)} pd_data["target"] = pd_data["target"].map(lambda t: old_target_to_new_target[t]) pd_data </code></pre></div> <div id="df-9b20fe83-aab5-475d-a40a-99d153076ba4" class="colab-df-container"> <div> <style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>filename</th> <th>fold</th> <th>target</th> <th>category</th> <th>esc10</th> <th>src_file</th> <th>take</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1-100032-A-0.wav</td> <td>1</td> <td>0</td> <td>dog</td> <td>True</td> <td>100032</td> <td>A</td> </tr> <tr> <th>14</th> <td>1-110389-A-0.wav</td> <td>1</td> <td>0</td> <td>dog</td> <td>True</td> <td>110389</td> <td>A</td> </tr> <tr> <th>24</th> <td>1-116765-A-41.wav</td> <td>1</td> <td>9</td> <td>chainsaw</td> <td>True</td> <td>116765</td> <td>A</td> </tr> <tr> <th>54</th> <td>1-17150-A-12.wav</td> <td>1</td> <td>4</td> <td>crackling_fire</td> <td>True</td> <td>17150</td> <td>A</td> </tr> <tr> <th>55</th> <td>1-172649-A-40.wav</td> <td>1</td> <td>8</td> <td>helicopter</td> <td>True</td> <td>172649</td> <td>A</td> </tr> <tr> <th>...</th> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> <tr> <th>1876</th> <td>5-233160-A-1.wav</td> <td>5</td> <td>1</td> <td>rooster</td> <td>True</td> <td>233160</td> <td>A</td> </tr> <tr> <th>1888</th> <td>5-234879-A-1.wav</td> <td>5</td> <td>1</td> <td>rooster</td> <td>True</td> <td>234879</td> <td>A</td> </tr> <tr> <th>1889</th> <td>5-234879-B-1.wav</td> <td>5</td> <td>1</td> <td>rooster</td> <td>True</td> <td>234879</td> <td>B</td> </tr> <tr> <th>1894</th> <td>5-235671-A-38.wav</td> <td>5</td> <td>7</td> <td>clock_tick</td> <td>True</td> <td>235671</td> <td>A</td> </tr> <tr> <th>1999</th> <td>5-9032-A-0.wav</td> <td>5</td> <td>0</td> <td>dog</td> <td>True</td> <td>9032</td> <td>A</td> </tr> </tbody> </table> 400 rows × 7 columns </div> <div class="colab-df-buttons"> <div class="colab-df-container"> <button class="colab-df-convert" onclick="convertToInteractive('df-9b20fe83-aab5-475d-a40a-99d153076ba4')" title="Convert this dataframe to an interactive table." style="display:none;"> <svg xmlns="http://www.w3.org/2000/svg" height="24px" viewBox="0 -960 960 960"> <path d="M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z"/> </svg> </button> <style> .colab-df-container { display:flex; gap: 12px; } .colab-df-convert { background-color: #E8F0FE; border: none; border-radius: 50%; cursor: pointer; display: none; fill: #1967D2; height: 32px; padding: 0 0 0 0; width: 32px; } .colab-df-convert:hover { background-color: #E2EBFA; box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15); fill: #174EA6; } .colab-df-buttons div { margin-bottom: 4px; } [theme=dark] .colab-df-convert { background-color: #3B4455; fill: #D2E3FC; } [theme=dark] .colab-df-convert:hover { background-color: #434B5C; box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15); filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3)); fill: #FFFFFF; } </style> <script> const buttonEl = document.querySelector('#df-9b20fe83-aab5-475d-a40a-99d153076ba4 button.colab-df-convert'); buttonEl.style.display = google.colab.kernel.accessAllowed ? 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'block' : 'none'; buttonEl.onclick = () => { google.colab.notebook.generateWithVariable('pd_data'); } })(); </script> </div> </div> </div> <h3 id="define-functions-to-read-and-preprocess-the-wav-files">Define functions to read and preprocess the WAV files</h3> <div class="codehilite"><pre><code>def read_wav_file(path, target_sr=SAMPLE_RATE): sr, wav = scipy.io.wavfile.read(os.path.join(BASE_DATA_DIR, "audio", path)) wav = wav.astype(np.float32) / 32768.0 # normalize to [-1, 1] num_samples = int(len(wav) * target_sr / sr) # resample to 16 kHz wav = resample(wav, num_samples) return wav[:, None] # Add a channel dimension (of size 1) </code></pre></div> Create a function that uses the <code>STFTSpectrogram</code> to compute a spectrogram, then plots it. <div class="codehilite"><pre><code>def plot_single_spectrogram(sample_wav_data): spectrogram = layers.STFTSpectrogram( mode="log", frame_length=SAMPLE_RATE * 20 // 1000, frame_step=SAMPLE_RATE * 5 // 1000, fft_length=1024, trainable=False, )(sample_wav_data[None, ...])[0, ...] # Plot the spectrogram plt.imshow(spectrogram.T, origin="lower") plt.title("Single Channel Spectrogram") plt.xlabel("Time") plt.ylabel("Frequency") plt.show() </code></pre></div> Create a function that uses the <code>STFTSpectrogram</code> to compute three spectrograms with multiple bandwidths, then aligns them as an image with different channels, to get a multi-bandwith spectrogram, then plots the spectrogram. <div class="codehilite"><pre><code>def plot_multi_bandwidth_spectrogram(sample_wav_data): # All spectrograms must use the same `fft_length`, `frame_step`, and # `padding="same"` in order to produce spectrograms with identical shapes, # hence aligning them together. `expand_dims` ensures that the shapes are # compatible with image models. spectrograms = np.concatenate( [ layers.STFTSpectrogram( mode="log", frame_length=SAMPLE_RATE * x // 1000, frame_step=SAMPLE_RATE * 5 // 1000, fft_length=1024, padding="same", expand_dims=True, )(sample_wav_data[None, ...])[0, ...] for x in [5, 10, 20] ], axis=-1, ).transpose([1, 0, 2]) # normalize each color channel for better viewing mn = spectrograms.min(axis=(0, 1), keepdims=True) mx = spectrograms.max(axis=(0, 1), keepdims=True) spectrograms = (spectrograms - mn) / (mx - mn) plt.imshow(spectrograms, origin="lower") plt.title("Multi-bandwidth Spectrogram") plt.xlabel("Time") plt.ylabel("Frequency") plt.show() </code></pre></div> Demonstrate a sample wav file. <div class="codehilite"><pre><code>sample_wav_data = read_wav_file(pd_data["filename"].tolist()[52]) plt.plot(sample_wav_data[:, 0]) plt.show() </code></pre></div> <img alt="png" src="https://github.com/keras-team/keras-io/blob/master/examples/audio/img/stft/raw_audio.png" /> Plot a Spectrogram <div class="codehilite"><pre><code>plot_single_spectrogram(sample_wav_data) </code></pre></div> <img alt="png" src="https://github.com/keras-team/keras-io/blob/master/examples/audio/img/stft/spectrogram.png" /> Plot a multi-bandwidth spectrogram <div class="codehilite"><pre><code>plot_multi_bandwidth_spectrogram(sample_wav_data) </code></pre></div> <img alt="png" src="https://github.com/keras-team/keras-io/blob/master/examples/audio/img/stft/multiband_spectrogram.png" /> <h3 id="define-functions-to-construct-a-tf-dataset">Define functions to construct a TF Dataset</h3> <div class="codehilite"><pre><code>def read_dataset(df, folds): msk = df["fold"].isin(folds) filenames = df["filename"][msk] targets = df["target"][msk].values waves = np.array([read_wav_file(fil) for fil in filenames], dtype=np.float32) return waves, targets </code></pre></div> <h3 id="create-the-datasets">Create the datasets</h3> <div class="codehilite"><pre><code>train_x, train_y = read_dataset(pd_data, [1, 2, 3]) valid_x, valid_y = read_dataset(pd_data, [4]) test_x, test_y = read_dataset(pd_data, [5]) </code></pre></div> <hr /> <h2 id="training-the-models">Training the Models</h2> In this tutorial we demonstrate the different usecases of the <code>STFTSpectrogram</code> layer. The first model will use a non-trainable <code>STFTSpectrogram</code> layer, so it is intended purely for preprocessing. Additionally, the model will use 1D signals, hence it make use of Conv1D layers. The second model will use a trainable <code>STFTSpectrogram</code> layer with the <code>expand_dims</code> option, which expands the shapes to be compatible with image models. <h3 id="create-the-1d-model">Create the 1D model</h3> <ol> <li>Create a non-trainable spectrograms, extracting a 1D time signal.</li> <li>Apply <code>Conv1D</code> layers with <code>LayerNormalization</code> simialar to the classic VGG design.</li> <li>Apply global maximum pooling to have fixed set of features.</li> <li>Add <code>Dense</code> layers to make the final predictions based on the features.</li> </ol> <div class="codehilite"><pre><code>model1d = keras.Sequential( [ layers.InputLayer((None, 1)), layers.STFTSpectrogram( mode="log", frame_length=SAMPLE_RATE * 40 // 1000, frame_step=SAMPLE_RATE * 15 // 1000, trainable=False, ), layers.Conv1D(64, 64, activation="relu"), layers.Conv1D(128, 16, activation="relu"), layers.LayerNormalization(), layers.MaxPooling1D(4), layers.Conv1D(128, 8, activation="relu"), layers.Conv1D(256, 8, activation="relu"), layers.Conv1D(512, 4, activation="relu"), layers.LayerNormalization(), layers.Dropout(0.5), layers.GlobalMaxPooling1D(), layers.Dense(256, activation="relu"), layers.Dense(256, activation="relu"), layers.Dropout(0.5), layers.Dense(NUM_CLASSES, activation="softmax"), ], name="model_1d_non_trainble_stft", ) model1d.compile( optimizer=keras.optimizers.Adam(1e-5), loss="sparse_categorical_crossentropy", metrics=["accuracy"], ) model1d.summary() </code></pre></div> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">Model: "model_1d_non_trainble_stft" </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ stft_spectrogram_4 (STFTSpectrogram) │ (None, None, 513) │ 656,640 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv1d (Conv1D) │ (None, None, 64) │ 2,101,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv1d_1 (Conv1D) │ (None, None, 128) │ 131,200 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ layer_normalization │ (None, None, 128) │ 256 │ │ (LayerNormalization) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ max_pooling1d (MaxPooling1D) │ (None, None, 128) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv1d_2 (Conv1D) │ (None, None, 128) │ 131,200 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv1d_3 (Conv1D) │ (None, None, 256) │ 262,400 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv1d_4 (Conv1D) │ (None, None, 512) │ 524,800 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ layer_normalization_1 │ (None, None, 512) │ 1,024 │ │ (LayerNormalization) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout (Dropout) │ (None, None, 512) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ global_max_pooling1d │ (None, 512) │ 0 │ │ (GlobalMaxPooling1D) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense (Dense) │ (None, 256) │ 131,328 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_1 (Dense) │ (None, 256) │ 65,792 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_1 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_2 (Dense) │ (None, 10) │ 2,570 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘ </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Total params: 4,008,522 (15.29 MB) </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Trainable params: 3,351,882 (12.79 MB) </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Non-trainable params: 656,640 (2.50 MB) </pre> Train the model and restore the best weights. <div class="codehilite"><pre><code>history_model1d = model1d.fit( train_x, train_y, batch_size=BATCH_SIZE, validation_data=(valid_x, valid_y), epochs=EPOCHS, callbacks=[ keras.callbacks.EarlyStopping( monitor="val_loss", patience=EPOCHS, restore_best_weights=True, ) ], ) </code></pre></div> <div class="codehilite"><pre><code>Epoch 1/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 271ms/step - accuracy: 0.1092 - loss: 3.1307 - val_accuracy: 0.0875 - val_loss: 2.4073 Epoch 2/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 6ms/step - accuracy: 0.1434 - loss: 2.6563 - val_accuracy: 0.1000 - val_loss: 2.4051 Epoch 3/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.1324 - loss: 2.5414 - val_accuracy: 0.1000 - val_loss: 2.4050 Epoch 4/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.1552 - loss: 2.4542 - val_accuracy: 0.1000 - val_loss: 2.3832 Epoch 5/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.1204 - loss: 2.3896 - val_accuracy: 0.1000 - val_loss: 2.3405 Epoch 6/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.1210 - loss: 2.3499 - val_accuracy: 0.1000 - val_loss: 2.3108 Epoch 7/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.1547 - loss: 2.2899 - val_accuracy: 0.1000 - val_loss: 2.2994 Epoch 8/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.1672 - loss: 2.2049 - val_accuracy: 0.1250 - val_loss: 2.2802 Epoch 9/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.2025 - loss: 2.1537 - val_accuracy: 0.1000 - val_loss: 2.2709 Epoch 10/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.1832 - loss: 2.1482 - val_accuracy: 0.1500 - val_loss: 2.2698 Epoch 11/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.2389 - loss: 2.0647 - val_accuracy: 0.1000 - val_loss: 2.2354 Epoch 12/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.2253 - loss: 1.9860 - val_accuracy: 0.2125 - val_loss: 2.1661 Epoch 13/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.2123 - loss: 2.0868 - val_accuracy: 0.1125 - val_loss: 2.1726 Epoch 14/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.2390 - loss: 2.0544 - val_accuracy: 0.2375 - val_loss: 2.1123 Epoch 15/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.2656 - loss: 2.0536 - val_accuracy: 0.2625 - val_loss: 2.1235 Epoch 16/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.3263 - loss: 1.9533 - val_accuracy: 0.1750 - val_loss: 2.1477 Epoch 17/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.3790 - loss: 1.8721 - val_accuracy: 0.1875 - val_loss: 2.0823 Epoch 18/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.3292 - loss: 1.8978 - val_accuracy: 0.3125 - val_loss: 2.0181 Epoch 19/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.3430 - loss: 1.8915 - val_accuracy: 0.3625 - val_loss: 1.9877 Epoch 20/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.3613 - loss: 1.7638 - val_accuracy: 0.3500 - val_loss: 1.9599 Epoch 21/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.4141 - loss: 1.6976 - val_accuracy: 0.4125 - val_loss: 1.9317 Epoch 22/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.4173 - loss: 1.6408 - val_accuracy: 0.3000 - val_loss: 1.9310 Epoch 23/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.3887 - loss: 1.5914 - val_accuracy: 0.4500 - val_loss: 1.8504 Epoch 24/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.3943 - loss: 1.5998 - val_accuracy: 0.2875 - val_loss: 1.8993 Epoch 25/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.5392 - loss: 1.4692 - val_accuracy: 0.4000 - val_loss: 1.8548 Epoch 26/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.4735 - loss: 1.5004 - val_accuracy: 0.4250 - val_loss: 1.8440 Epoch 27/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.5132 - loss: 1.4321 - val_accuracy: 0.5000 - val_loss: 1.7961 Epoch 28/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.5147 - loss: 1.3093 - val_accuracy: 0.4250 - val_loss: 1.8132 Epoch 29/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.5344 - loss: 1.3614 - val_accuracy: 0.5000 - val_loss: 1.7522 Epoch 30/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.5545 - loss: 1.2561 - val_accuracy: 0.5375 - val_loss: 1.7180 Epoch 31/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.5697 - loss: 1.2651 - val_accuracy: 0.5500 - val_loss: 1.6538 Epoch 32/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.5385 - loss: 1.2571 - val_accuracy: 0.6125 - val_loss: 1.6453 Epoch 33/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.5734 - loss: 1.3083 - val_accuracy: 0.5125 - val_loss: 1.6801 Epoch 34/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.5976 - loss: 1.1720 - val_accuracy: 0.4625 - val_loss: 1.6860 Epoch 35/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.5268 - loss: 1.3844 - val_accuracy: 0.6375 - val_loss: 1.6253 Epoch 36/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.6021 - loss: 1.1720 - val_accuracy: 0.4625 - val_loss: 1.7012 Epoch 37/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.5144 - loss: 1.2672 - val_accuracy: 0.6250 - val_loss: 1.5866 Epoch 38/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.6075 - loss: 1.1400 - val_accuracy: 0.6125 - val_loss: 1.5615 Epoch 39/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.6272 - loss: 1.1138 - val_accuracy: 0.5000 - val_loss: 1.6364 Epoch 40/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.5718 - loss: 1.1956 - val_accuracy: 0.6000 - val_loss: 1.6239 Epoch 41/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.5934 - loss: 1.1302 - val_accuracy: 0.5250 - val_loss: 1.5490 Epoch 42/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.5930 - loss: 1.0970 - val_accuracy: 0.5625 - val_loss: 1.5530 Epoch 43/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.6369 - loss: 0.9976 - val_accuracy: 0.6375 - val_loss: 1.5028 Epoch 44/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.6918 - loss: 0.9205 - val_accuracy: 0.6625 - val_loss: 1.4681 Epoch 45/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.6543 - loss: 0.9118 - val_accuracy: 0.6000 - val_loss: 1.4737 Epoch 46/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.6243 - loss: 1.0268 - val_accuracy: 0.5750 - val_loss: 1.5423 Epoch 47/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.6391 - loss: 1.0181 - val_accuracy: 0.6625 - val_loss: 1.4783 Epoch 48/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.6863 - loss: 0.9874 - val_accuracy: 0.7000 - val_loss: 1.3977 Epoch 49/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.7209 - loss: 0.8359 - val_accuracy: 0.6625 - val_loss: 1.3844 Epoch 50/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.7659 - loss: 0.8241 - val_accuracy: 0.6500 - val_loss: 1.4206 Epoch 51/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.7143 - loss: 0.8972 - val_accuracy: 0.6750 - val_loss: 1.3756 Epoch 52/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.7081 - loss: 0.9544 - val_accuracy: 0.6375 - val_loss: 1.3703 Epoch 53/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.6907 - loss: 0.9446 - val_accuracy: 0.6750 - val_loss: 1.3564 Epoch 54/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.7460 - loss: 0.7399 - val_accuracy: 0.6000 - val_loss: 1.3840 Epoch 55/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.7293 - loss: 0.8620 - val_accuracy: 0.6000 - val_loss: 1.3743 Epoch 56/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.7504 - loss: 0.7715 - val_accuracy: 0.6875 - val_loss: 1.3175 Epoch 57/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.7643 - loss: 0.7617 - val_accuracy: 0.6625 - val_loss: 1.3407 Epoch 58/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.7568 - loss: 0.7798 - val_accuracy: 0.6875 - val_loss: 1.2950 Epoch 59/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.7863 - loss: 0.6884 - val_accuracy: 0.6625 - val_loss: 1.3306 Epoch 60/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.7550 - loss: 0.7504 - val_accuracy: 0.6500 - val_loss: 1.3260 Epoch 61/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8069 - loss: 0.6624 - val_accuracy: 0.6375 - val_loss: 1.3168 Epoch 62/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.7089 - loss: 0.8183 - val_accuracy: 0.7500 - val_loss: 1.2525 Epoch 63/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - accuracy: 0.7407 - loss: 0.7860 - val_accuracy: 0.7000 - val_loss: 1.2101 Epoch 64/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.7526 - loss: 0.7691 - val_accuracy: 0.7250 - val_loss: 1.2327 Epoch 65/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.7827 - loss: 0.7485 - val_accuracy: 0.6750 - val_loss: 1.2848 Epoch 66/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.7195 - loss: 0.7853 - val_accuracy: 0.7000 - val_loss: 1.2047 Epoch 67/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.7539 - loss: 0.7530 - val_accuracy: 0.7125 - val_loss: 1.1954 Epoch 68/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.7912 - loss: 0.6220 - val_accuracy: 0.6750 - val_loss: 1.2297 Epoch 69/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.7688 - loss: 0.6403 - val_accuracy: 0.6375 - val_loss: 1.2524 Epoch 70/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.7699 - loss: 0.7181 - val_accuracy: 0.6625 - val_loss: 1.2147 Epoch 71/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.8300 - loss: 0.5858 - val_accuracy: 0.7000 - val_loss: 1.1705 Epoch 72/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - accuracy: 0.7518 - loss: 0.6276 - val_accuracy: 0.7625 - val_loss: 1.1478 Epoch 73/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8192 - loss: 0.5830 - val_accuracy: 0.6750 - val_loss: 1.1484 Epoch 74/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8044 - loss: 0.6725 - val_accuracy: 0.7500 - val_loss: 1.1518 Epoch 75/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.7974 - loss: 0.5536 - val_accuracy: 0.6625 - val_loss: 1.2326 Epoch 76/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.7249 - loss: 0.7748 - val_accuracy: 0.7500 - val_loss: 1.1622 Epoch 77/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8083 - loss: 0.5952 - val_accuracy: 0.7125 - val_loss: 1.1240 Epoch 78/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8133 - loss: 0.5249 - val_accuracy: 0.7000 - val_loss: 1.1463 Epoch 79/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8088 - loss: 0.5889 - val_accuracy: 0.7375 - val_loss: 1.0684 Epoch 80/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8715 - loss: 0.4484 - val_accuracy: 0.7500 - val_loss: 1.0295 Epoch 81/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.8099 - loss: 0.5720 - val_accuracy: 0.7125 - val_loss: 1.0846 Epoch 82/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8377 - loss: 0.5405 - val_accuracy: 0.7250 - val_loss: 1.0810 Epoch 83/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.7981 - loss: 0.5354 - val_accuracy: 0.7250 - val_loss: 1.0617 Epoch 84/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.7894 - loss: 0.5246 - val_accuracy: 0.7625 - val_loss: 1.0503 Epoch 85/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8695 - loss: 0.4168 - val_accuracy: 0.7125 - val_loss: 1.1376 Epoch 86/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.7566 - loss: 0.6546 - val_accuracy: 0.7250 - val_loss: 1.0920 Epoch 87/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8146 - loss: 0.5367 - val_accuracy: 0.6750 - val_loss: 1.0721 Epoch 88/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.8836 - loss: 0.4781 - val_accuracy: 0.7625 - val_loss: 1.0165 Epoch 89/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.8691 - loss: 0.4114 - val_accuracy: 0.7500 - val_loss: 0.9928 Epoch 90/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.8794 - loss: 0.4078 - val_accuracy: 0.7750 - val_loss: 0.9922 Epoch 91/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8698 - loss: 0.4249 - val_accuracy: 0.7375 - val_loss: 1.0113 Epoch 92/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8553 - loss: 0.4388 - val_accuracy: 0.6875 - val_loss: 1.1355 Epoch 93/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8322 - loss: 0.5300 - val_accuracy: 0.7375 - val_loss: 1.0236 Epoch 94/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9123 - loss: 0.4124 - val_accuracy: 0.7625 - val_loss: 0.9826 Epoch 95/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8403 - loss: 0.4664 - val_accuracy: 0.7750 - val_loss: 0.9689 Epoch 96/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8281 - loss: 0.4742 - val_accuracy: 0.7250 - val_loss: 1.1120 Epoch 97/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8416 - loss: 0.4398 - val_accuracy: 0.7375 - val_loss: 1.0888 Epoch 98/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8671 - loss: 0.4704 - val_accuracy: 0.6625 - val_loss: 1.0802 Epoch 99/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.8976 - loss: 0.3859 - val_accuracy: 0.8000 - val_loss: 0.9549 Epoch 100/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8579 - loss: 0.4120 - val_accuracy: 0.7000 - val_loss: 1.0427 Epoch 101/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8420 - loss: 0.4820 - val_accuracy: 0.7500 - val_loss: 0.9615 Epoch 102/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8501 - loss: 0.4540 - val_accuracy: 0.7625 - val_loss: 0.9078 Epoch 103/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8569 - loss: 0.3727 - val_accuracy: 0.6750 - val_loss: 0.9443 Epoch 104/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9123 - loss: 0.2994 - val_accuracy: 0.6875 - val_loss: 0.9821 Epoch 105/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8797 - loss: 0.3424 - val_accuracy: 0.7750 - val_loss: 0.9252 Epoch 106/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8501 - loss: 0.4048 - val_accuracy: 0.7750 - val_loss: 0.9589 Epoch 107/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8604 - loss: 0.3666 - val_accuracy: 0.7375 - val_loss: 0.9306 Epoch 108/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9082 - loss: 0.3093 - val_accuracy: 0.7250 - val_loss: 0.9925 Epoch 109/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.8382 - loss: 0.4424 - val_accuracy: 0.7875 - val_loss: 0.8926 Epoch 110/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9047 - loss: 0.3130 - val_accuracy: 0.7375 - val_loss: 0.9806 Epoch 111/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8886 - loss: 0.3073 - val_accuracy: 0.7375 - val_loss: 0.9880 Epoch 112/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9027 - loss: 0.3040 - val_accuracy: 0.6875 - val_loss: 1.0214 Epoch 113/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8932 - loss: 0.4064 - val_accuracy: 0.7125 - val_loss: 1.0849 Epoch 114/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8624 - loss: 0.4336 - val_accuracy: 0.8000 - val_loss: 0.9287 Epoch 115/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8925 - loss: 0.4030 - val_accuracy: 0.7625 - val_loss: 0.9044 Epoch 116/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.8922 - loss: 0.3145 - val_accuracy: 0.7750 - val_loss: 0.8441 Epoch 117/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9369 - loss: 0.2919 - val_accuracy: 0.7625 - val_loss: 0.8530 Epoch 118/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9051 - loss: 0.2753 - val_accuracy: 0.7250 - val_loss: 0.9205 Epoch 119/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9144 - loss: 0.2948 - val_accuracy: 0.7000 - val_loss: 0.9843 Epoch 120/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9043 - loss: 0.3258 - val_accuracy: 0.7125 - val_loss: 0.9686 Epoch 121/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9383 - loss: 0.2482 - val_accuracy: 0.7125 - val_loss: 0.9158 Epoch 122/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9314 - loss: 0.3248 - val_accuracy: 0.7000 - val_loss: 1.0416 Epoch 123/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8713 - loss: 0.3495 - val_accuracy: 0.7125 - val_loss: 0.9176 Epoch 124/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8660 - loss: 0.3550 - val_accuracy: 0.7750 - val_loss: 0.9248 Epoch 125/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9375 - loss: 0.2040 - val_accuracy: 0.7875 - val_loss: 0.8526 Epoch 126/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9521 - loss: 0.2011 - val_accuracy: 0.7750 - val_loss: 0.8185 Epoch 127/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9070 - loss: 0.2604 - val_accuracy: 0.7875 - val_loss: 0.8706 Epoch 128/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8554 - loss: 0.3367 - val_accuracy: 0.6750 - val_loss: 1.0503 Epoch 129/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8305 - loss: 0.5195 - val_accuracy: 0.7500 - val_loss: 0.9261 Epoch 130/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8939 - loss: 0.3566 - val_accuracy: 0.7875 - val_loss: 0.8478 Epoch 131/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9220 - loss: 0.2700 - val_accuracy: 0.7625 - val_loss: 0.8353 Epoch 132/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.8607 - loss: 0.3409 - val_accuracy: 0.7750 - val_loss: 0.8898 Epoch 133/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8637 - loss: 0.3109 - val_accuracy: 0.7125 - val_loss: 0.9377 Epoch 134/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.8967 - loss: 0.3634 - val_accuracy: 0.7500 - val_loss: 0.9168 Epoch 135/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9148 - loss: 0.2964 - val_accuracy: 0.7250 - val_loss: 0.8667 Epoch 136/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9322 - loss: 0.2350 - val_accuracy: 0.7625 - val_loss: 0.8509 Epoch 137/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9591 - loss: 0.1990 - val_accuracy: 0.8125 - val_loss: 0.7958 Epoch 138/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9115 - loss: 0.2270 - val_accuracy: 0.7250 - val_loss: 0.8488 Epoch 139/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9749 - loss: 0.1524 - val_accuracy: 0.7750 - val_loss: 0.7888 Epoch 140/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9682 - loss: 0.1539 - val_accuracy: 0.8125 - val_loss: 0.7912 Epoch 141/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9379 - loss: 0.1751 - val_accuracy: 0.8125 - val_loss: 0.8002 Epoch 142/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9681 - loss: 0.1103 - val_accuracy: 0.7750 - val_loss: 0.7951 Epoch 143/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9728 - loss: 0.1513 - val_accuracy: 0.7125 - val_loss: 0.8118 Epoch 144/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9460 - loss: 0.1630 - val_accuracy: 0.8125 - val_loss: 0.7843 Epoch 145/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9627 - loss: 0.1494 - val_accuracy: 0.7625 - val_loss: 0.8179 Epoch 146/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9207 - loss: 0.2203 - val_accuracy: 0.7500 - val_loss: 0.8580 Epoch 147/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9507 - loss: 0.1636 - val_accuracy: 0.7875 - val_loss: 0.7897 Epoch 148/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9562 - loss: 0.1523 - val_accuracy: 0.7625 - val_loss: 0.7950 Epoch 149/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9643 - loss: 0.1464 - val_accuracy: 0.7500 - val_loss: 0.8591 Epoch 150/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9449 - loss: 0.1604 - val_accuracy: 0.7250 - val_loss: 0.9112 Epoch 151/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.9043 - loss: 0.2253 - val_accuracy: 0.7875 - val_loss: 0.7553 Epoch 152/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9459 - loss: 0.1466 - val_accuracy: 0.7250 - val_loss: 0.7929 Epoch 153/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9509 - loss: 0.1329 - val_accuracy: 0.8000 - val_loss: 0.7272 Epoch 154/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9458 - loss: 0.2293 - val_accuracy: 0.7500 - val_loss: 0.7482 Epoch 155/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9596 - loss: 0.1434 - val_accuracy: 0.7750 - val_loss: 0.7726 Epoch 156/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9428 - loss: 0.1471 - val_accuracy: 0.8250 - val_loss: 0.7562 Epoch 157/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9775 - loss: 0.1568 - val_accuracy: 0.7625 - val_loss: 0.7586 Epoch 158/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9256 - loss: 0.1936 - val_accuracy: 0.7750 - val_loss: 0.8041 Epoch 159/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9507 - loss: 0.1620 - val_accuracy: 0.7000 - val_loss: 0.9265 Epoch 160/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9545 - loss: 0.2093 - val_accuracy: 0.7875 - val_loss: 0.7786 Epoch 161/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9428 - loss: 0.1747 - val_accuracy: 0.7250 - val_loss: 0.8367 Epoch 162/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9377 - loss: 0.2172 - val_accuracy: 0.7625 - val_loss: 0.7964 Epoch 163/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9509 - loss: 0.1753 - val_accuracy: 0.7500 - val_loss: 0.7437 Epoch 164/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9694 - loss: 0.1197 - val_accuracy: 0.7750 - val_loss: 0.7330 Epoch 165/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9594 - loss: 0.1065 - val_accuracy: 0.7375 - val_loss: 0.8036 Epoch 166/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9752 - loss: 0.1265 - val_accuracy: 0.7000 - val_loss: 0.8316 Epoch 167/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9121 - loss: 0.1863 - val_accuracy: 0.7500 - val_loss: 0.7953 Epoch 168/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9320 - loss: 0.1759 - val_accuracy: 0.8000 - val_loss: 0.8142 Epoch 169/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9613 - loss: 0.1785 - val_accuracy: 0.7625 - val_loss: 0.7585 Epoch 170/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9666 - loss: 0.1096 - val_accuracy: 0.7875 - val_loss: 0.7595 Epoch 171/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9518 - loss: 0.1422 - val_accuracy: 0.7875 - val_loss: 0.7417 Epoch 172/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9689 - loss: 0.1236 - val_accuracy: 0.7625 - val_loss: 0.7539 Epoch 173/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.9959 - loss: 0.0662 - val_accuracy: 0.7875 - val_loss: 0.6840 Epoch 174/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9835 - loss: 0.0803 - val_accuracy: 0.7500 - val_loss: 0.7929 Epoch 175/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9319 - loss: 0.1924 - val_accuracy: 0.7500 - val_loss: 0.8044 Epoch 176/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9290 - loss: 0.2342 - val_accuracy: 0.8000 - val_loss: 0.7280 Epoch 177/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9446 - loss: 0.1692 - val_accuracy: 0.7500 - val_loss: 0.7537 Epoch 178/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9868 - loss: 0.0925 - val_accuracy: 0.8000 - val_loss: 0.7145 Epoch 179/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9788 - loss: 0.1382 - val_accuracy: 0.7625 - val_loss: 0.7860 Epoch 180/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9771 - loss: 0.0829 - val_accuracy: 0.8125 - val_loss: 0.6933 Epoch 181/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9602 - loss: 0.1095 - val_accuracy: 0.7750 - val_loss: 0.7213 Epoch 182/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9723 - loss: 0.1172 - val_accuracy: 0.7500 - val_loss: 0.7286 Epoch 183/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9532 - loss: 0.1564 - val_accuracy: 0.7875 - val_loss: 0.7060 Epoch 184/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.9789 - loss: 0.0840 - val_accuracy: 0.8125 - val_loss: 0.6554 Epoch 185/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9857 - loss: 0.0764 - val_accuracy: 0.7875 - val_loss: 0.7785 Epoch 186/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9849 - loss: 0.0791 - val_accuracy: 0.7625 - val_loss: 0.7358 Epoch 187/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9702 - loss: 0.0919 - val_accuracy: 0.7500 - val_loss: 0.7888 Epoch 188/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9931 - loss: 0.0779 - val_accuracy: 0.7625 - val_loss: 0.7874 Epoch 189/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9604 - loss: 0.1247 - val_accuracy: 0.7875 - val_loss: 0.7642 Epoch 190/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9402 - loss: 0.1906 - val_accuracy: 0.7875 - val_loss: 0.8763 Epoch 191/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9845 - loss: 0.1111 - val_accuracy: 0.7875 - val_loss: 0.6824 Epoch 192/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9899 - loss: 0.0591 - val_accuracy: 0.8000 - val_loss: 0.6591 Epoch 193/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9716 - loss: 0.1055 - val_accuracy: 0.7625 - val_loss: 0.7776 Epoch 194/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9750 - loss: 0.0953 - val_accuracy: 0.7250 - val_loss: 0.7947 Epoch 195/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.9765 - loss: 0.0889 - val_accuracy: 0.7375 - val_loss: 0.7190 Epoch 196/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9741 - loss: 0.0896 - val_accuracy: 0.8000 - val_loss: 0.7058 Epoch 197/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9586 - loss: 0.0916 - val_accuracy: 0.7625 - val_loss: 0.7676 Epoch 198/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9955 - loss: 0.0655 - val_accuracy: 0.7625 - val_loss: 0.7047 Epoch 199/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9861 - loss: 0.0663 - val_accuracy: 0.7750 - val_loss: 0.7760 Epoch 200/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step - accuracy: 0.9982 - loss: 0.0558 - val_accuracy: 0.7750 - val_loss: 0.6585 </code></pre></div> <h3 id="create-the-2d-model">Create the 2D model</h3> <ol> <li>Create three spectrograms with multiple band-widths from the raw input.</li> <li>Concatenate the three spectrograms to have three channels.</li> <li>Load <code>MobileNet</code> and set the weights from the weights trained on <code>ImageNet</code>.</li> <li>Apply global maximum pooling to have fixed set of features.</li> <li>Add <code>Dense</code> layers to make the final predictions based on the features.</li> </ol> <div class="codehilite"><pre><code>input = layers.Input((None, 1)) spectrograms = [ layers.STFTSpectrogram( mode="log", frame_length=SAMPLE_RATE * frame_size // 1000, frame_step=SAMPLE_RATE * 15 // 1000, fft_length=2048, padding="same", expand_dims=True, # trainable=True, # trainable by default )(input) for frame_size in [30, 40, 50] # frame size in milliseconds ] multi_spectrograms = layers.Concatenate(axis=-1)(spectrograms) img_model = keras.applications.MobileNet(include_top=False, pooling="max") output = img_model(multi_spectrograms) output = layers.Dropout(0.5)(output) output = layers.Dense(256, activation="relu")(output) output = layers.Dense(256, activation="relu")(output) output = layers.Dense(NUM_CLASSES, activation="softmax")(output) model2d = keras.Model(input, output, name="model_2d_trainble_stft") model2d.compile( optimizer=keras.optimizers.Adam(1e-4), loss="sparse_categorical_crossentropy", metrics=["accuracy"], ) model2d.summary() </code></pre></div> <div class="codehilite"><pre><code><ipython-input-16-bf7092b3c6d2>:17: UserWarning: `input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default. img_model = keras.applications.MobileNet(include_top=False, pooling="max") </code></pre></div> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">Model: "model_2d_trainble_stft" </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ │ input_layer_1 │ (None, None, 1) │ 0 │ - │ │ (InputLayer) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ stft_spectrogram_5 │ (None, None, 1025, 1) │ 984,000 │ input_layer_1[0][0] │ │ (STFTSpectrogram) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ stft_spectrogram_6 │ (None, None, 1025, 1) │ 1,312,000 │ input_layer_1[0][0] │ │ (STFTSpectrogram) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ stft_spectrogram_7 │ (None, None, 1025, 1) │ 1,640,000 │ input_layer_1[0][0] │ │ (STFTSpectrogram) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ concatenate (Concatenate) │ (None, None, 1025, 3) │ 0 │ stft_spectrogram_5[0]… │ │ │ │ │ stft_spectrogram_6[0]… │ │ │ │ │ stft_spectrogram_7[0]… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ mobilenet_1.00_224 │ (None, 1024) │ 3,228,864 │ concatenate[0][0] │ │ (Functional) │ │ │ │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ dropout_2 (Dropout) │ (None, 1024) │ 0 │ mobilenet_1.00_224[0]… │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ dense_3 (Dense) │ (None, 256) │ 262,400 │ dropout_2[0][0] │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ dense_4 (Dense) │ (None, 256) │ 65,792 │ dense_3[0][0] │ ├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤ │ dense_5 (Dense) │ (None, 10) │ 2,570 │ dense_4[0][0] │ └───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘ </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Total params: 7,495,626 (28.59 MB) </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Trainable params: 7,473,738 (28.51 MB) </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Non-trainable params: 21,888 (85.50 KB) </pre> Train the model and restore the best weights. <div class="codehilite"><pre><code>history_model2d = model2d.fit( train_x, train_y, batch_size=BATCH_SIZE, validation_data=(valid_x, valid_y), epochs=EPOCHS, callbacks=[ keras.callbacks.EarlyStopping( monitor="val_loss", patience=EPOCHS, restore_best_weights=True, ) ], ) </code></pre></div> <div class="codehilite"><pre><code>Epoch 1/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m50s[0m 776ms/step - accuracy: 0.0855 - loss: 7.6484 - val_accuracy: 0.0625 - val_loss: 3.7484 Epoch 2/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 55ms/step - accuracy: 0.1293 - loss: 5.8848 - val_accuracy: 0.0750 - val_loss: 4.0622 Epoch 3/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.1302 - loss: 4.6363 - val_accuracy: 0.0875 - val_loss: 3.6488 Epoch 4/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.1656 - loss: 4.6861 - val_accuracy: 0.1250 - val_loss: 3.5224 Epoch 5/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.2025 - loss: 4.3601 - val_accuracy: 0.0875 - val_loss: 4.0424 Epoch 6/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.2072 - loss: 3.8723 - val_accuracy: 0.1125 - val_loss: 3.1530 Epoch 7/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.2562 - loss: 3.2596 - val_accuracy: 0.1125 - val_loss: 2.9712 Epoch 8/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.2328 - loss: 3.1374 - val_accuracy: 0.1375 - val_loss: 3.0128 Epoch 9/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.3296 - loss: 2.6887 - val_accuracy: 0.1750 - val_loss: 2.6742 Epoch 10/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.3123 - loss: 2.4022 - val_accuracy: 0.1750 - val_loss: 2.7165 Epoch 11/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.3781 - loss: 2.3441 - val_accuracy: 0.1875 - val_loss: 2.1900 Epoch 12/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.4524 - loss: 2.0044 - val_accuracy: 0.3250 - val_loss: 1.8786 Epoch 13/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.3609 - loss: 2.0790 - val_accuracy: 0.3750 - val_loss: 1.7390 Epoch 14/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.5158 - loss: 1.6717 - val_accuracy: 0.3750 - val_loss: 1.5660 Epoch 15/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.5080 - loss: 1.6551 - val_accuracy: 0.4125 - val_loss: 1.6085 Epoch 16/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.5921 - loss: 1.4493 - val_accuracy: 0.5250 - val_loss: 1.2603 Epoch 17/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.5404 - loss: 1.4931 - val_accuracy: 0.6000 - val_loss: 1.0863 Epoch 18/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.6492 - loss: 1.0411 - val_accuracy: 0.6000 - val_loss: 1.0920 Epoch 19/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.5987 - loss: 1.3023 - val_accuracy: 0.5625 - val_loss: 1.0882 Epoch 20/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.5950 - loss: 1.2483 - val_accuracy: 0.5500 - val_loss: 1.0755 Epoch 21/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.5789 - loss: 1.1988 - val_accuracy: 0.5875 - val_loss: 0.9171 Epoch 22/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.6694 - loss: 1.0415 - val_accuracy: 0.6875 - val_loss: 0.8319 Epoch 23/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 53ms/step - accuracy: 0.7705 - loss: 0.8017 - val_accuracy: 0.6750 - val_loss: 0.8824 Epoch 24/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.6693 - loss: 1.0069 - val_accuracy: 0.7500 - val_loss: 0.6454 Epoch 25/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.6997 - loss: 0.8689 - val_accuracy: 0.7250 - val_loss: 0.7640 Epoch 26/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.6816 - loss: 0.8254 - val_accuracy: 0.7500 - val_loss: 0.6418 Epoch 27/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.6524 - loss: 1.1302 - val_accuracy: 0.7375 - val_loss: 0.7160 Epoch 28/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.7624 - loss: 0.7522 - val_accuracy: 0.7875 - val_loss: 0.6805 Epoch 29/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.6926 - loss: 0.8897 - val_accuracy: 0.7500 - val_loss: 0.6289 Epoch 30/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.7190 - loss: 0.7467 - val_accuracy: 0.7375 - val_loss: 0.5838 Epoch 31/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.7171 - loss: 0.7727 - val_accuracy: 0.8250 - val_loss: 0.6101 Epoch 32/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.8120 - loss: 0.5287 - val_accuracy: 0.8625 - val_loss: 0.4229 Epoch 33/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.7921 - loss: 0.5581 - val_accuracy: 0.8250 - val_loss: 0.4174 Epoch 34/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.8056 - loss: 0.5415 - val_accuracy: 0.8500 - val_loss: 0.4672 Epoch 35/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 50ms/step - accuracy: 0.7601 - loss: 0.5661 - val_accuracy: 0.8250 - val_loss: 0.4791 Epoch 36/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.7866 - loss: 0.5135 - val_accuracy: 0.8750 - val_loss: 0.4217 Epoch 37/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.8660 - loss: 0.3952 - val_accuracy: 0.8250 - val_loss: 0.4561 Epoch 38/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.8446 - loss: 0.3751 - val_accuracy: 0.9000 - val_loss: 0.3954 Epoch 39/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.8546 - loss: 0.3984 - val_accuracy: 0.8375 - val_loss: 0.4534 Epoch 40/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.8655 - loss: 0.3541 - val_accuracy: 0.8875 - val_loss: 0.3718 Epoch 41/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.8592 - loss: 0.4164 - val_accuracy: 0.8750 - val_loss: 0.4537 Epoch 42/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9093 - loss: 0.2404 - val_accuracy: 0.8625 - val_loss: 0.4169 Epoch 43/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.9329 - loss: 0.1855 - val_accuracy: 0.8750 - val_loss: 0.3354 Epoch 44/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.8353 - loss: 0.4455 - val_accuracy: 0.8750 - val_loss: 0.3619 Epoch 45/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.9135 - loss: 0.2196 - val_accuracy: 0.8750 - val_loss: 0.3313 Epoch 46/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.9129 - loss: 0.2131 - val_accuracy: 0.8875 - val_loss: 0.3199 Epoch 47/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.9467 - loss: 0.1264 - val_accuracy: 0.8875 - val_loss: 0.3162 Epoch 48/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.9281 - loss: 0.2276 - val_accuracy: 0.8875 - val_loss: 0.3158 Epoch 49/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9211 - loss: 0.2044 - val_accuracy: 0.8375 - val_loss: 0.3702 Epoch 50/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.9247 - loss: 0.1954 - val_accuracy: 0.8750 - val_loss: 0.2875 Epoch 51/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.9534 - loss: 0.1122 - val_accuracy: 0.9000 - val_loss: 0.2637 Epoch 52/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.9596 - loss: 0.1261 - val_accuracy: 0.9125 - val_loss: 0.2370 Epoch 53/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9388 - loss: 0.1679 - val_accuracy: 0.9125 - val_loss: 0.2506 Epoch 54/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9635 - loss: 0.1075 - val_accuracy: 0.9125 - val_loss: 0.2656 Epoch 55/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9511 - loss: 0.1666 - val_accuracy: 0.9000 - val_loss: 0.2998 Epoch 56/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9688 - loss: 0.0860 - val_accuracy: 0.9000 - val_loss: 0.2730 Epoch 57/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9786 - loss: 0.0796 - val_accuracy: 0.8875 - val_loss: 0.2837 Epoch 58/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9421 - loss: 0.1239 - val_accuracy: 0.8750 - val_loss: 0.2829 Epoch 59/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9392 - loss: 0.2626 - val_accuracy: 0.8750 - val_loss: 0.3105 Epoch 60/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9395 - loss: 0.1321 - val_accuracy: 0.9000 - val_loss: 0.2529 Epoch 61/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9679 - loss: 0.0968 - val_accuracy: 0.8750 - val_loss: 0.2506 Epoch 62/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9437 - loss: 0.1074 - val_accuracy: 0.9000 - val_loss: 0.2950 Epoch 63/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9615 - loss: 0.0958 - val_accuracy: 0.8750 - val_loss: 0.3064 Epoch 64/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9755 - loss: 0.0601 - val_accuracy: 0.9000 - val_loss: 0.2795 Epoch 65/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.9723 - loss: 0.0673 - val_accuracy: 0.9125 - val_loss: 0.2123 Epoch 66/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.9464 - loss: 0.1619 - val_accuracy: 0.9375 - val_loss: 0.1930 Epoch 67/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.9863 - loss: 0.0445 - val_accuracy: 0.9250 - val_loss: 0.1866 Epoch 68/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9823 - loss: 0.0678 - val_accuracy: 0.9125 - val_loss: 0.2109 Epoch 69/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9855 - loss: 0.0579 - val_accuracy: 0.9375 - val_loss: 0.2088 Epoch 70/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.9800 - loss: 0.0549 - val_accuracy: 0.9625 - val_loss: 0.1693 Epoch 71/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9861 - loss: 0.0469 - val_accuracy: 0.9500 - val_loss: 0.1738 Epoch 72/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9876 - loss: 0.0685 - val_accuracy: 0.9375 - val_loss: 0.2090 Epoch 73/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9605 - loss: 0.0835 - val_accuracy: 0.8875 - val_loss: 0.2828 Epoch 74/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9783 - loss: 0.0475 - val_accuracy: 0.8875 - val_loss: 0.2500 Epoch 75/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9871 - loss: 0.0470 - val_accuracy: 0.9000 - val_loss: 0.2094 Epoch 76/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9881 - loss: 0.0405 - val_accuracy: 0.9500 - val_loss: 0.1971 Epoch 77/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 45ms/step - accuracy: 0.9736 - loss: 0.0418 - val_accuracy: 0.9375 - val_loss: 0.2014 Epoch 78/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9582 - loss: 0.1145 - val_accuracy: 0.9125 - val_loss: 0.2082 Epoch 79/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9831 - loss: 0.0586 - val_accuracy: 0.9125 - val_loss: 0.2109 Epoch 80/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9574 - loss: 0.0950 - val_accuracy: 0.9000 - val_loss: 0.3043 Epoch 81/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9964 - loss: 0.0253 - val_accuracy: 0.9250 - val_loss: 0.2476 Epoch 82/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9838 - loss: 0.0427 - val_accuracy: 0.9125 - val_loss: 0.2480 Epoch 83/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0094 - val_accuracy: 0.9250 - val_loss: 0.2614 Epoch 84/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9929 - loss: 0.0256 - val_accuracy: 0.9250 - val_loss: 0.2504 Epoch 85/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9953 - loss: 0.0215 - val_accuracy: 0.9250 - val_loss: 0.2334 Epoch 86/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9939 - loss: 0.0200 - val_accuracy: 0.9500 - val_loss: 0.2138 Epoch 87/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0133 - val_accuracy: 0.9500 - val_loss: 0.2167 Epoch 88/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9907 - loss: 0.0303 - val_accuracy: 0.9125 - val_loss: 0.2326 Epoch 89/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9883 - loss: 0.0406 - val_accuracy: 0.9500 - val_loss: 0.2000 Epoch 90/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9932 - loss: 0.0292 - val_accuracy: 0.9375 - val_loss: 0.1961 Epoch 91/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9756 - loss: 0.1435 - val_accuracy: 0.9375 - val_loss: 0.2093 Epoch 92/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9762 - loss: 0.0868 - val_accuracy: 0.9375 - val_loss: 0.2081 Epoch 93/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9925 - loss: 0.0391 - val_accuracy: 0.9375 - val_loss: 0.1890 Epoch 94/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9961 - loss: 0.0324 - val_accuracy: 0.9250 - val_loss: 0.2047 Epoch 95/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9955 - loss: 0.0208 - val_accuracy: 0.8875 - val_loss: 0.2223 Epoch 96/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9841 - loss: 0.0363 - val_accuracy: 0.9125 - val_loss: 0.1951 Epoch 97/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9835 - loss: 0.0384 - val_accuracy: 0.9250 - val_loss: 0.1983 Epoch 98/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9801 - loss: 0.0662 - val_accuracy: 0.9375 - val_loss: 0.2212 Epoch 99/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9957 - loss: 0.0206 - val_accuracy: 0.9125 - val_loss: 0.2114 Epoch 100/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9947 - loss: 0.0318 - val_accuracy: 0.9125 - val_loss: 0.1936 Epoch 101/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0153 - val_accuracy: 0.9250 - val_loss: 0.1731 Epoch 102/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9946 - loss: 0.0219 - val_accuracy: 0.9250 - val_loss: 0.1804 Epoch 103/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 1.0000 - loss: 0.0092 - val_accuracy: 0.9125 - val_loss: 0.1641 Epoch 104/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 45ms/step - accuracy: 0.9811 - loss: 0.0325 - val_accuracy: 0.9250 - val_loss: 0.1796 Epoch 105/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9850 - loss: 0.0276 - val_accuracy: 0.9375 - val_loss: 0.1738 Epoch 106/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0074 - val_accuracy: 0.9125 - val_loss: 0.1991 Epoch 107/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9873 - loss: 0.0487 - val_accuracy: 0.9125 - val_loss: 0.1900 Epoch 108/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 45ms/step - accuracy: 0.9951 - loss: 0.0224 - val_accuracy: 0.9000 - val_loss: 0.1935 Epoch 109/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9790 - loss: 0.0544 - val_accuracy: 0.9375 - val_loss: 0.1995 Epoch 110/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0061 - val_accuracy: 0.9375 - val_loss: 0.1956 Epoch 111/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9968 - loss: 0.0158 - val_accuracy: 0.9375 - val_loss: 0.1800 Epoch 112/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9912 - loss: 0.0273 - val_accuracy: 0.9125 - val_loss: 0.1894 Epoch 113/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9939 - loss: 0.0118 - val_accuracy: 0.9250 - val_loss: 0.1858 Epoch 114/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9943 - loss: 0.0308 - val_accuracy: 0.9250 - val_loss: 0.1713 Epoch 115/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9950 - loss: 0.0152 - val_accuracy: 0.9250 - val_loss: 0.1794 Epoch 116/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0084 - val_accuracy: 0.9375 - val_loss: 0.1895 Epoch 117/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.9947 - loss: 0.0174 - val_accuracy: 0.9500 - val_loss: 0.1563 Epoch 118/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 1.0000 - loss: 0.0055 - val_accuracy: 0.9500 - val_loss: 0.1477 Epoch 119/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9763 - loss: 0.0478 - val_accuracy: 0.9000 - val_loss: 0.1918 Epoch 120/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9958 - loss: 0.0135 - val_accuracy: 0.8875 - val_loss: 0.2846 Epoch 121/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9934 - loss: 0.0334 - val_accuracy: 0.9375 - val_loss: 0.1980 Epoch 122/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9943 - loss: 0.0203 - val_accuracy: 0.9500 - val_loss: 0.1832 Epoch 123/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9801 - loss: 0.0573 - val_accuracy: 0.9250 - val_loss: 0.2416 Epoch 124/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9949 - loss: 0.0334 - val_accuracy: 0.9375 - val_loss: 0.1865 Epoch 125/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.9933 - loss: 0.0120 - val_accuracy: 0.9500 - val_loss: 0.1340 Epoch 126/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9944 - loss: 0.0126 - val_accuracy: 0.9250 - val_loss: 0.1565 Epoch 127/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 45ms/step - accuracy: 0.9949 - loss: 0.0143 - val_accuracy: 0.9125 - val_loss: 0.2242 Epoch 128/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9941 - loss: 0.0138 - val_accuracy: 0.9500 - val_loss: 0.1581 Epoch 129/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.9992 - loss: 0.0128 - val_accuracy: 0.9500 - val_loss: 0.1274 Epoch 130/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9966 - loss: 0.0123 - val_accuracy: 0.9625 - val_loss: 0.1514 Epoch 131/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9873 - loss: 0.0401 - val_accuracy: 0.9375 - val_loss: 0.1517 Epoch 132/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9784 - loss: 0.0407 - val_accuracy: 0.9375 - val_loss: 0.1771 Epoch 133/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9982 - loss: 0.0108 - val_accuracy: 0.9250 - val_loss: 0.2291 Epoch 134/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9957 - loss: 0.0185 - val_accuracy: 0.9000 - val_loss: 0.3030 Epoch 135/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9771 - loss: 0.0511 - val_accuracy: 0.9250 - val_loss: 0.2313 Epoch 136/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9965 - loss: 0.0162 - val_accuracy: 0.9375 - val_loss: 0.1983 Epoch 137/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9829 - loss: 0.0797 - val_accuracy: 0.9500 - val_loss: 0.1685 Epoch 138/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9910 - loss: 0.0352 - val_accuracy: 0.9625 - val_loss: 0.1578 Epoch 139/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9818 - loss: 0.0346 - val_accuracy: 0.9375 - val_loss: 0.1616 Epoch 140/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0079 - val_accuracy: 0.9375 - val_loss: 0.1702 Epoch 141/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0095 - val_accuracy: 0.9750 - val_loss: 0.1386 Epoch 142/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.9987 - loss: 0.0081 - val_accuracy: 0.9750 - val_loss: 0.1187 Epoch 143/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0020 - val_accuracy: 0.9750 - val_loss: 0.1209 Epoch 144/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 49ms/step - accuracy: 0.9763 - loss: 0.0806 - val_accuracy: 0.9625 - val_loss: 0.1177 Epoch 145/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9905 - loss: 0.0263 - val_accuracy: 0.9125 - val_loss: 0.2067 Epoch 146/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0086 - val_accuracy: 0.9125 - val_loss: 0.2563 Epoch 147/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9746 - loss: 0.1065 - val_accuracy: 0.9375 - val_loss: 0.2253 Epoch 148/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9799 - loss: 0.0885 - val_accuracy: 0.9625 - val_loss: 0.1564 Epoch 149/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9955 - loss: 0.0290 - val_accuracy: 0.9250 - val_loss: 0.2414 Epoch 150/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9727 - loss: 0.0846 - val_accuracy: 0.9125 - val_loss: 0.2415 Epoch 151/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9973 - loss: 0.0157 - val_accuracy: 0.9000 - val_loss: 0.3168 Epoch 152/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9827 - loss: 0.0280 - val_accuracy: 0.9125 - val_loss: 0.2191 Epoch 153/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9856 - loss: 0.0289 - val_accuracy: 0.9500 - val_loss: 0.1684 Epoch 154/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9993 - loss: 0.0128 - val_accuracy: 0.9625 - val_loss: 0.1246 Epoch 155/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.9918 - loss: 0.0194 - val_accuracy: 0.9625 - val_loss: 0.0904 Epoch 156/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 48ms/step - accuracy: 0.9992 - loss: 0.0125 - val_accuracy: 0.9625 - val_loss: 0.0854 Epoch 157/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9986 - loss: 0.0083 - val_accuracy: 0.9500 - val_loss: 0.0979 Epoch 158/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0062 - val_accuracy: 0.9625 - val_loss: 0.1077 Epoch 159/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9949 - loss: 0.0305 - val_accuracy: 0.9625 - val_loss: 0.1058 Epoch 160/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9976 - loss: 0.0084 - val_accuracy: 0.9625 - val_loss: 0.1202 Epoch 161/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0030 - val_accuracy: 0.9625 - val_loss: 0.1031 Epoch 162/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9714 - loss: 0.0519 - val_accuracy: 0.9625 - val_loss: 0.1832 Epoch 163/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0016 - val_accuracy: 0.9250 - val_loss: 0.2786 Epoch 164/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 45ms/step - accuracy: 0.9733 - loss: 0.0312 - val_accuracy: 0.8750 - val_loss: 0.2878 Epoch 165/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9897 - loss: 0.0452 - val_accuracy: 0.9375 - val_loss: 0.1482 Epoch 166/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9956 - loss: 0.0164 - val_accuracy: 0.9500 - val_loss: 0.1278 Epoch 167/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9934 - loss: 0.0399 - val_accuracy: 0.9375 - val_loss: 0.2300 Epoch 168/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9900 - loss: 0.0420 - val_accuracy: 0.8875 - val_loss: 0.5143 Epoch 169/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9869 - loss: 0.0500 - val_accuracy: 0.9125 - val_loss: 0.2374 Epoch 170/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9849 - loss: 0.0366 - val_accuracy: 0.9125 - val_loss: 0.3109 Epoch 171/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9918 - loss: 0.0244 - val_accuracy: 0.8875 - val_loss: 0.2994 Epoch 172/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9979 - loss: 0.0061 - val_accuracy: 0.9375 - val_loss: 0.2885 Epoch 173/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0073 - val_accuracy: 0.9375 - val_loss: 0.3030 Epoch 174/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9795 - loss: 0.0277 - val_accuracy: 0.8750 - val_loss: 0.4379 Epoch 175/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9966 - loss: 0.0176 - val_accuracy: 0.8750 - val_loss: 0.3758 Epoch 176/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9973 - loss: 0.0046 - val_accuracy: 0.9375 - val_loss: 0.2478 Epoch 177/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0043 - val_accuracy: 0.9375 - val_loss: 0.2529 Epoch 178/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0041 - val_accuracy: 0.9250 - val_loss: 0.2604 Epoch 179/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9973 - loss: 0.0068 - val_accuracy: 0.8875 - val_loss: 0.2902 Epoch 180/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9866 - loss: 0.0297 - val_accuracy: 0.8625 - val_loss: 0.3225 Epoch 181/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9935 - loss: 0.0085 - val_accuracy: 0.9000 - val_loss: 0.3310 Epoch 182/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9930 - loss: 0.0230 - val_accuracy: 0.8875 - val_loss: 0.4211 Epoch 183/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9981 - loss: 0.0054 - val_accuracy: 0.9125 - val_loss: 0.2929 Epoch 184/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0136 - val_accuracy: 0.9375 - val_loss: 0.2564 Epoch 185/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9907 - loss: 0.0160 - val_accuracy: 0.9000 - val_loss: 0.2726 Epoch 186/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9992 - loss: 0.0036 - val_accuracy: 0.9000 - val_loss: 0.2530 Epoch 187/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0051 - val_accuracy: 0.9250 - val_loss: 0.2283 Epoch 188/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0036 - val_accuracy: 0.9250 - val_loss: 0.2084 Epoch 189/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0012 - val_accuracy: 0.9250 - val_loss: 0.2196 Epoch 190/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0090 - val_accuracy: 0.9375 - val_loss: 0.2332 Epoch 191/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9981 - loss: 0.0096 - val_accuracy: 0.9250 - val_loss: 0.2485 Epoch 192/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9878 - loss: 0.0368 - val_accuracy: 0.9125 - val_loss: 0.3140 Epoch 193/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0013 - val_accuracy: 0.9125 - val_loss: 0.3289 Epoch 194/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0091 - val_accuracy: 0.9125 - val_loss: 0.3065 Epoch 195/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9947 - loss: 0.0131 - val_accuracy: 0.9125 - val_loss: 0.2800 Epoch 196/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9928 - loss: 0.0078 - val_accuracy: 0.9125 - val_loss: 0.2394 Epoch 197/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9957 - loss: 0.0133 - val_accuracy: 0.9000 - val_loss: 0.2319 Epoch 198/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 0.9993 - loss: 0.0031 - val_accuracy: 0.9125 - val_loss: 0.2119 Epoch 199/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0014 - val_accuracy: 0.9375 - val_loss: 0.2095 Epoch 200/200 [1m15/15[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 46ms/step - accuracy: 1.0000 - loss: 0.0042 - val_accuracy: 0.9375 - val_loss: 0.1972 </code></pre></div> <h3 id="plot-training-history">Plot Training History</h3> <div class="codehilite"><pre><code>epochs_range = range(EPOCHS) plt.figure(figsize=(14, 5)) plt.subplot(1, 2, 1) plt.plot( epochs_range, history_model1d.history["accuracy"], label="Training Accuracy,1D model with non-trainable STFT", ) plt.plot( epochs_range, history_model1d.history["val_accuracy"], label="Validation Accuracy, 1D model with non-trainable STFT", ) plt.plot( epochs_range, history_model2d.history["accuracy"], label="Training Accuracy, 2D model with trainable STFT", ) plt.plot( epochs_range, history_model2d.history["val_accuracy"], label="Validation Accuracy, 2D model with trainable STFT", ) plt.legend(loc="lower right") plt.title("Training and Validation Accuracy") plt.subplot(1, 2, 2) plt.plot( epochs_range, history_model1d.history["loss"], label="Training Loss,1D model with non-trainable STFT", ) plt.plot( epochs_range, history_model1d.history["val_loss"], label="Validation Loss, 1D model with non-trainable STFT", ) plt.plot( epochs_range, history_model2d.history["loss"], label="Training Loss, 2D model with trainable STFT", ) plt.plot( epochs_range, history_model2d.history["val_loss"], label="Validation Loss, 2D model with trainable STFT", ) plt.legend(loc="upper right") plt.title("Training and Validation Loss") plt.show() </code></pre></div> <img alt="png" src="https://github.com/keras-team/keras-io/blob/master/examples/audio/img/stft/training.png" /> <h3 id="evaluate-on-test-data">Evaluate on Test Data</h3> Running the models on the test set. <div class="codehilite"><pre><code>_, test_acc = model1d.evaluate(test_x, test_y) print(f"1D model wit non-trainable STFT -> Test Accuracy: {test_acc * 100:.2f}%") </code></pre></div> <div class="codehilite"><pre><code>[1m3/3[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 307ms/step - accuracy: 0.8148 - loss: 0.6244 1D model wit non-trainable STFT -> Test Accuracy: 82.50% </code></pre></div> <div class="codehilite"><pre><code>_, test_acc = model2d.evaluate(test_x, test_y) print(f"2D model with trainable STFT -> Test Accuracy: {test_acc * 100:.2f}%") </code></pre></div> <div class="codehilite"><pre><code>[1m3/3[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m17s[0m 546ms/step - accuracy: 0.9195 - loss: 0.5271 2D model with trainable STFT -> Test Accuracy: 92.50% </code></pre></div> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#audio-classification-with-the-stftspectrogram-layer'>Audio Classification with the STFTSpectrogram layer</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#introduction'>Introduction</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#setup'>Setup</a> </div> <div class='k-outline-depth-3'> <a href='#importing-the-necessary-libraries'>Importing the necessary libraries</a> </div> <div class='k-outline-depth-3'> <a href='#define-some-variables'>Define some variables</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#download-and-preprocess-the-esc10-dataset'>Download and Preprocess the ESC-10 Dataset</a> </div> <div class='k-outline-depth-3'> <a href='#download-and-extract-the-dataset'>Download and Extract the dataset</a> </div> <div class='k-outline-depth-3'> <a href='#read-the-csv-file'>Read the CSV file</a> </div> <div class='k-outline-depth-3'> <a href='#define-functions-to-read-and-preprocess-the-wav-files'>Define functions to read and preprocess the WAV files</a> </div> <div class='k-outline-depth-3'> <a href='#define-functions-to-construct-a-tf-dataset'>Define functions to construct a TF Dataset</a> </div> <div class='k-outline-depth-3'> <a href='#create-the-datasets'>Create the datasets</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#training-the-models'>Training the Models</a> </div> <div class='k-outline-depth-3'> <a href='#create-the-1d-model'>Create the 1D model</a> </div> <div class='k-outline-depth-3'> <a href='#create-the-2d-model'>Create the 2D model</a> </div> <div class='k-outline-depth-3'> <a href='#plot-training-history'>Plot Training History</a> </div> <div class='k-outline-depth-3'> <a href='#evaluate-on-test-data'>Evaluate on Test Data</a> </div> </div> </div> </div> </div> </body> <footer style="float: left; width: 100%; padding: 1em; border-top: solid 1px #bbb;"> <a href="https://policies.google.com/terms">Terms</a> | <a href="https://policies.google.com/privacy">Privacy</a> </footer> </html>