{
"cells": [
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2025-06-13T18:30:29.866142Z",
"start_time": "2025-06-13T18:30:25.905632Z"
}
},
"source": [
"import pandas as pd\n",
"from pathlib import Path\n",
"import tensorflow as tf\n",
"import keras"
],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-06-13 21:30:27.024673: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
"2025-06-13 21:30:27.030485: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
"2025-06-13 21:30:27.098384: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
"2025-06-13 21:30:27.126152: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
"E0000 00:00:1749839427.186777 173645 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"E0000 00:00:1749839427.198620 173645 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"W0000 00:00:1749839427.299296 173645 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"W0000 00:00:1749839427.299363 173645 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"W0000 00:00:1749839427.299370 173645 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"W0000 00:00:1749839427.299374 173645 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"2025-06-13 21:30:27.308420: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
}
],
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-13T18:30:30.549387Z",
"start_time": "2025-06-13T18:30:30.017053Z"
}
},
"cell_type": "code",
"source": [
"data_path = Path(\"Data/\")\n",
"\n",
"train_files = sorted(data_path.glob(\"train/chorale_*.csv\"))\n",
"valid_files = sorted(data_path.glob(\"valid/chorale_*.csv\"))\n",
"test_files = sorted(data_path.glob(\"test/chorale_*.csv\"))\n",
"\n",
"\n",
"def load_chorales(filepaths):\n",
" return [pd.read_csv(filepath).values.tolist() for filepath in filepaths]\n",
"\n",
"train_chorales = load_chorales(train_files)\n",
"valid_chorales = load_chorales(valid_files)\n",
"test_chorales = load_chorales(test_files)"
],
"id": "c11c09065ed24a79",
"outputs": [],
"execution_count": 2
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-13T18:30:30.614479Z",
"start_time": "2025-06-13T18:30:30.573871Z"
}
},
"cell_type": "code",
"source": [
"notes = set()\n",
"for chorales in (train_chorales, valid_chorales, test_chorales):\n",
" for chorale in chorales:\n",
" for chord in chorale:\n",
" notes |= set(chord)\n",
"\n",
"n_notes = len(notes)\n",
"min_note = min(notes - {0})\n",
"max_note = max(notes)\n",
"\n",
"assert min_note == 36\n",
"assert max_note == 81"
],
"id": "bd3a64ccd37d231e",
"outputs": [],
"execution_count": 3
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-13T18:30:30.739118Z",
"start_time": "2025-06-13T18:30:30.728629Z"
}
},
"cell_type": "code",
"source": [
"def create_target(batch):\n",
" X = batch[:, :-1]\n",
" Y = batch[:, 1:] # predict next note in each arpegio, at each step\n",
" return X, Y\n",
"\n",
"def preprocess(window):\n",
" window = tf.where(window == 0, window, window - min_note + 1) # shift values\n",
" return tf.reshape(window, [-1]) # convert to arpegio\n",
"\n",
"def bach_dataset(chorales, batch_size=32, shuffle_buffer_size=None,\n",
" window_size=32, window_shift=16, cache=True):\n",
" def batch_window(window):\n",
" return window.batch(window_size + 1)\n",
"\n",
" def to_windows(chorale):\n",
" dataset = tf.data.Dataset.from_tensor_slices(chorale)\n",
" dataset = dataset.window(window_size + 1, window_shift, drop_remainder=True)\n",
" return dataset.flat_map(batch_window)\n",
"\n",
" chorales = tf.ragged.constant(chorales, ragged_rank=1)\n",
" dataset = tf.data.Dataset.from_tensor_slices(chorales)\n",
" dataset = dataset.flat_map(to_windows).map(preprocess)\n",
" if cache:\n",
" dataset = dataset.cache()\n",
" if shuffle_buffer_size:\n",
" dataset = dataset.shuffle(shuffle_buffer_size)\n",
" dataset = dataset.batch(batch_size)\n",
" dataset = dataset.map(create_target)\n",
" return dataset.prefetch(1)"
],
"id": "10ec7c14c413e81",
"outputs": [],
"execution_count": 4
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-13T18:30:32.291591Z",
"start_time": "2025-06-13T18:30:30.842156Z"
}
},
"cell_type": "code",
"source": [
"train_set = bach_dataset(train_chorales, shuffle_buffer_size=1000)\n",
"valid_set = bach_dataset(valid_chorales)\n",
"test_set = bach_dataset(test_chorales)"
],
"id": "b0324d31a18d79b1",
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-06-13 21:30:31.566353: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n"
]
}
],
"execution_count": 5
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-13T18:30:32.571773Z",
"start_time": "2025-06-13T18:30:32.311130Z"
}
},
"cell_type": "code",
"source": [
"model = keras.Sequential(name=\"Music_RNN\", layers=[\n",
" keras.layers.Embedding(input_dim=n_notes, output_dim=5, input_shape=[None]),\n",
" keras.layers.Conv1D(32, kernel_size=2, padding=\"causal\", activation=\"relu\"),\n",
" keras.layers.BatchNormalization(),\n",
" keras.layers.Conv1D(48, kernel_size=2, padding=\"causal\", activation=\"relu\", dilation_rate=2),\n",
" keras.layers.BatchNormalization(),\n",
" keras.layers.Conv1D(64, kernel_size=2, padding=\"causal\", activation=\"relu\", dilation_rate=4),\n",
" keras.layers.BatchNormalization(),\n",
" keras.layers.Conv1D(96, kernel_size=2, padding=\"causal\", activation=\"relu\", dilation_rate=8),\n",
" keras.layers.BatchNormalization(),\n",
" keras.layers.Conv1D(128, kernel_size=2, padding=\"causal\", activation=\"relu\", dilation_rate=16),\n",
" keras.layers.BatchNormalization(),\n",
" keras.layers.LSTM(256, return_sequences=True),\n",
" keras.layers.Dense(n_notes, activation=\"softmax\")\n",
"])\n",
"\n",
"model.summary()"
],
"id": "f2e6ee5949d5c394",
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/andrei0016/miniconda3/lib/python3.12/site-packages/keras/src/layers/core/embedding.py:93: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(**kwargs)\n"
]
},
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],
"execution_count": 6
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-13T18:39:05.590442Z",
"start_time": "2025-06-13T18:30:32.654092Z"
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"cell_type": "code",
"source": [
"optimizer = tf.keras.optimizers.Nadam(learning_rate=1e-3)\n",
"model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"])\n",
"model.fit(train_set, epochs=20, validation_data=valid_set)"
],
"id": "f92ec5b749cda8d6",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/20\n",
" 98/Unknown \u001B[1m28s\u001B[0m 223ms/step - accuracy: 0.3137 - loss: 2.6649"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-06-13 21:31:00.707129: I tensorflow/core/framework/local_rendezvous.cc:407] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
"\t [[{{node IteratorGetNext}}]]\n",
"/home/andrei0016/miniconda3/lib/python3.12/contextlib.py:158: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
" self.gen.throw(value)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m32s\u001B[0m 260ms/step - accuracy: 0.3156 - loss: 2.6569 - val_accuracy: 0.0786 - val_loss: 3.7352\n",
"Epoch 2/20\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-06-13 21:31:04.276111: I tensorflow/core/framework/local_rendezvous.cc:407] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
"\t [[{{node IteratorGetNext}}]]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m25s\u001B[0m 254ms/step - accuracy: 0.7535 - loss: 0.9438 - val_accuracy: 0.1163 - val_loss: 3.4637\n",
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"text": [
"2025-06-13 21:31:29.153759: I tensorflow/core/framework/local_rendezvous.cc:407] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
"\t [[{{node IteratorGetNext}}]]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m25s\u001B[0m 254ms/step - accuracy: 0.7917 - loss: 0.7477 - val_accuracy: 0.1727 - val_loss: 3.1308\n",
"Epoch 4/20\n",
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m26s\u001B[0m 264ms/step - accuracy: 0.8093 - loss: 0.6566 - val_accuracy: 0.2591 - val_loss: 2.5800\n",
"Epoch 5/20\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-06-13 21:32:19.866931: I tensorflow/core/framework/local_rendezvous.cc:407] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
"\t [[{{node IteratorGetNext}}]]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m25s\u001B[0m 254ms/step - accuracy: 0.8241 - loss: 0.5948 - val_accuracy: 0.5689 - val_loss: 1.4124\n",
"Epoch 6/20\n",
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m25s\u001B[0m 258ms/step - accuracy: 0.8339 - loss: 0.5554 - val_accuracy: 0.7331 - val_loss: 0.8900\n",
"Epoch 7/20\n",
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m25s\u001B[0m 260ms/step - accuracy: 0.8438 - loss: 0.5161 - val_accuracy: 0.7983 - val_loss: 0.6799\n",
"Epoch 8/20\n",
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m26s\u001B[0m 262ms/step - accuracy: 0.8522 - loss: 0.4868 - val_accuracy: 0.8114 - val_loss: 0.6398\n",
"Epoch 9/20\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-06-13 21:34:01.066670: I tensorflow/core/framework/local_rendezvous.cc:407] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
"\t [[{{node IteratorGetNext}}]]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m25s\u001B[0m 259ms/step - accuracy: 0.8592 - loss: 0.4599 - val_accuracy: 0.8212 - val_loss: 0.6050\n",
"Epoch 10/20\n",
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m25s\u001B[0m 254ms/step - accuracy: 0.8688 - loss: 0.4258 - val_accuracy: 0.8226 - val_loss: 0.6068\n",
"Epoch 11/20\n",
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m25s\u001B[0m 260ms/step - accuracy: 0.8758 - loss: 0.4037 - val_accuracy: 0.8244 - val_loss: 0.6088\n",
"Epoch 12/20\n",
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m26s\u001B[0m 261ms/step - accuracy: 0.8813 - loss: 0.3820 - val_accuracy: 0.8236 - val_loss: 0.6096\n",
"Epoch 13/20\n",
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m26s\u001B[0m 261ms/step - accuracy: 0.8891 - loss: 0.3574 - val_accuracy: 0.8212 - val_loss: 0.6176\n",
"Epoch 14/20\n",
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m26s\u001B[0m 261ms/step - accuracy: 0.8954 - loss: 0.3381 - val_accuracy: 0.8231 - val_loss: 0.6219\n",
"Epoch 15/20\n",
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m26s\u001B[0m 262ms/step - accuracy: 0.9038 - loss: 0.3131 - val_accuracy: 0.8211 - val_loss: 0.6362\n",
"Epoch 16/20\n",
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m26s\u001B[0m 262ms/step - accuracy: 0.9090 - loss: 0.2961 - val_accuracy: 0.8198 - val_loss: 0.6408\n",
"Epoch 17/20\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-06-13 21:37:24.919507: I tensorflow/core/framework/local_rendezvous.cc:407] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
"\t [[{{node IteratorGetNext}}]]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m25s\u001B[0m 254ms/step - accuracy: 0.9140 - loss: 0.2782 - val_accuracy: 0.8175 - val_loss: 0.6539\n",
"Epoch 18/20\n",
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m25s\u001B[0m 256ms/step - accuracy: 0.9198 - loss: 0.2611 - val_accuracy: 0.8178 - val_loss: 0.6626\n",
"Epoch 19/20\n",
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m25s\u001B[0m 258ms/step - accuracy: 0.9250 - loss: 0.2443 - val_accuracy: 0.8115 - val_loss: 0.6911\n",
"Epoch 20/20\n",
"\u001B[1m98/98\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m25s\u001B[0m 259ms/step - accuracy: 0.9269 - loss: 0.2380 - val_accuracy: 0.8130 - val_loss: 0.6963\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 7
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-13T18:39:08.893890Z",
"start_time": "2025-06-13T18:39:05.658624Z"
}
},
"cell_type": "code",
"source": "model.evaluate(test_set)",
"id": "27ab3b7bf89d06a5",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[1m34/34\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 92ms/step - accuracy: 0.8088 - loss: 0.7133\n"
]
},
{
"data": {
"text/plain": [
"[0.7032589316368103, 0.8107198476791382]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 8
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-13T18:39:08.918957Z",
"start_time": "2025-06-13T18:39:08.913926Z"
}
},
"cell_type": "code",
"source": [
"def generate_chorale(model, seed_chords, length, temperature=1):\n",
" arpegio = preprocess(tf.constant(seed_chords, dtype=tf.int64))\n",
" arpegio = tf.reshape(arpegio, [1, -1])\n",
" for chord in range(length):\n",
" for note in range(4):\n",
" next_note_probas = model.predict(arpegio)[0, -1:]\n",
" rescaled_logits = tf.math.log(next_note_probas) / temperature\n",
" next_note = tf.random.categorical(rescaled_logits, num_samples=1)\n",
" arpegio = tf.concat([arpegio, next_note], axis=1)\n",
" arpegio = tf.where(arpegio == 0, arpegio, arpegio + min_note - 1)\n",
" return tf.reshape(arpegio, shape=[-1, 4])"
],
"id": "7aaa9e9471ea7ce1",
"outputs": [],
"execution_count": 9
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-13T18:39:08.985392Z",
"start_time": "2025-06-13T18:39:08.974660Z"
}
},
"cell_type": "code",
"source": [
"import numpy as np\n",
"from IPython.display import Audio\n",
"\n",
"def notes_to_frequencies(notes):\n",
" # Frequency doubles when you go up one octave; there are 12 semi-tones\n",
" # per octave; Note A on octave 4 is 440 Hz, and it is note number 69.\n",
" return 2 ** ((np.array(notes) - 69) / 12) * 440\n",
"\n",
"def frequencies_to_samples(frequencies, tempo, sample_rate):\n",
" note_duration = 60 / tempo # the tempo is measured in beats per minutes\n",
" # To reduce click sound at every beat, we round the frequencies to try to\n",
" # get the samples close to zero at the end of each note.\n",
" frequencies = (note_duration * frequencies).round() / note_duration\n",
" n_samples = int(note_duration * sample_rate)\n",
" time = np.linspace(0, note_duration, n_samples)\n",
" sine_waves = np.sin(2 * np.pi * frequencies.reshape(-1, 1) * time)\n",
" # Removing all notes with frequencies ≤ 9 Hz (includes note 0 = silence)\n",
" sine_waves *= (frequencies > 9.).reshape(-1, 1)\n",
" return sine_waves.reshape(-1)\n",
"\n",
"def chords_to_samples(chords, tempo, sample_rate):\n",
" freqs = notes_to_frequencies(chords)\n",
" freqs = np.r_[freqs, freqs[-1:]] # make last note a bit longer\n",
" merged = np.mean([frequencies_to_samples(melody, tempo, sample_rate)\n",
" for melody in freqs.T], axis=0)\n",
" n_fade_out_samples = sample_rate * 60 // tempo # fade out last note\n",
" fade_out = np.linspace(1., 0., n_fade_out_samples)**2\n",
" merged[-n_fade_out_samples:] *= fade_out\n",
" return merged\n",
"\n",
"def play_chords(chords, tempo=160, amplitude=0.1, sample_rate=44100, filepath=None):\n",
" samples = amplitude * chords_to_samples(chords, tempo, sample_rate)\n",
" if filepath:\n",
" from scipy.io import wavfile\n",
" samples = (2**15 * samples).astype(np.int16)\n",
" wavfile.write(filepath, sample_rate, samples)\n",
" return display(Audio(filepath))\n",
" else:\n",
" return display(Audio(samples, rate=sample_rate))"
],
"id": "e5b24894eccd06d3",
"outputs": [],
"execution_count": 10
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-13T18:39:27.914689Z",
"start_time": "2025-06-13T18:39:09.031574Z"
}
},
"cell_type": "code",
"source": [
"seed_chords = test_chorales[3][:8]\n",
"new_chorale_v2_cold = generate_chorale(model, seed_chords, 56, temperature=1.5)\n",
"play_chords(new_chorale_v2_cold, filepath=\"bach.wav\")"
],
"id": "d8e95bd07acf37c8",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 421ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 479ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
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"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 34ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 32ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 40ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 32ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 33ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 34ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 36ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 32ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 32ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 34ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 33ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 34ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 34ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 38ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 34ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 34ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 33ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 32ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 33ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 36ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 33ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 34ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 32ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 33ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 33ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 33ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 39ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 39ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 37ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 34ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 40ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 36ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 38ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 36ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 36ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 41ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 41ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 36ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 40ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 41ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 38ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 37ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 38ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 38ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 38ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 37ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 40ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 43ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 36ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 34ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 37ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 41ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 37ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 39ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 38ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 43ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 38ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 37ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 41ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 37ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 41ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 39ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 41ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 34ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 35ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 40ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 39ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 37ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 38ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 43ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 38ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 42ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 42ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 43ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 41ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 38ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 39ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 45ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 40ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 41ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 38ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 40ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 44ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 40ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 40ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 40ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 39ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 43ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 43ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 37ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 39ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 45ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 46ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 44ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 45ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 42ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 46ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 48ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 41ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 46ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 48ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 47ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 40ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 38ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 39ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 39ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 41ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 44ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 39ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 40ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 38ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 46ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 43ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 45ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 42ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 38ms/step\n",
"\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 41ms/step\n",
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