import pandas as pd
from pathlib import Path
import tensorflow as tf
import keras
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`. 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. 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. 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 WARNING: All log messages before absl::InitializeLog() is called are written to STDERR 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 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 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. 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. 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. 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. 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. To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
data_path = Path("Data/")
train_files = sorted(data_path.glob("train/chorale_*.csv"))
valid_files = sorted(data_path.glob("valid/chorale_*.csv"))
test_files = sorted(data_path.glob("test/chorale_*.csv"))
def load_chorales(filepaths):
return [pd.read_csv(filepath).values.tolist() for filepath in filepaths]
train_chorales = load_chorales(train_files)
valid_chorales = load_chorales(valid_files)
test_chorales = load_chorales(test_files)
notes = set()
for chorales in (train_chorales, valid_chorales, test_chorales):
for chorale in chorales:
for chord in chorale:
notes |= set(chord)
n_notes = len(notes)
min_note = min(notes - {0})
max_note = max(notes)
assert min_note == 36
assert max_note == 81
def create_target(batch):
X = batch[:, :-1]
Y = batch[:, 1:] # predict next note in each arpegio, at each step
return X, Y
def preprocess(window):
window = tf.where(window == 0, window, window - min_note + 1) # shift values
return tf.reshape(window, [-1]) # convert to arpegio
def bach_dataset(chorales, batch_size=32, shuffle_buffer_size=None,
window_size=32, window_shift=16, cache=True):
def batch_window(window):
return window.batch(window_size + 1)
def to_windows(chorale):
dataset = tf.data.Dataset.from_tensor_slices(chorale)
dataset = dataset.window(window_size + 1, window_shift, drop_remainder=True)
return dataset.flat_map(batch_window)
chorales = tf.ragged.constant(chorales, ragged_rank=1)
dataset = tf.data.Dataset.from_tensor_slices(chorales)
dataset = dataset.flat_map(to_windows).map(preprocess)
if cache:
dataset = dataset.cache()
if shuffle_buffer_size:
dataset = dataset.shuffle(shuffle_buffer_size)
dataset = dataset.batch(batch_size)
dataset = dataset.map(create_target)
return dataset.prefetch(1)
train_set = bach_dataset(train_chorales, shuffle_buffer_size=1000)
valid_set = bach_dataset(valid_chorales)
test_set = bach_dataset(test_chorales)
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)
model = keras.Sequential(name="Music_RNN", layers=[
keras.layers.Embedding(input_dim=n_notes, output_dim=5, input_shape=[None]),
keras.layers.Conv1D(32, kernel_size=2, padding="causal", activation="relu"),
keras.layers.BatchNormalization(),
keras.layers.Conv1D(48, kernel_size=2, padding="causal", activation="relu", dilation_rate=2),
keras.layers.BatchNormalization(),
keras.layers.Conv1D(64, kernel_size=2, padding="causal", activation="relu", dilation_rate=4),
keras.layers.BatchNormalization(),
keras.layers.Conv1D(96, kernel_size=2, padding="causal", activation="relu", dilation_rate=8),
keras.layers.BatchNormalization(),
keras.layers.Conv1D(128, kernel_size=2, padding="causal", activation="relu", dilation_rate=16),
keras.layers.BatchNormalization(),
keras.layers.LSTM(256, return_sequences=True),
keras.layers.Dense(n_notes, activation="softmax")
])
model.summary()
/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. super().__init__(**kwargs)
Model: "Music_RNN"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ embedding (Embedding) │ (None, None, 5) │ 235 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv1d (Conv1D) │ (None, None, 32) │ 352 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization │ (None, None, 32) │ 128 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv1d_1 (Conv1D) │ (None, None, 48) │ 3,120 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization_1 │ (None, None, 48) │ 192 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv1d_2 (Conv1D) │ (None, None, 64) │ 6,208 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization_2 │ (None, None, 64) │ 256 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv1d_3 (Conv1D) │ (None, None, 96) │ 12,384 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization_3 │ (None, None, 96) │ 384 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv1d_4 (Conv1D) │ (None, None, 128) │ 24,704 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ batch_normalization_4 │ (None, None, 128) │ 512 │ │ (BatchNormalization) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ lstm (LSTM) │ (None, None, 256) │ 394,240 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense (Dense) │ (None, None, 47) │ 12,079 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 454,794 (1.73 MB)
Trainable params: 454,058 (1.73 MB)
Non-trainable params: 736 (2.88 KB)
optimizer = tf.keras.optimizers.Nadam(learning_rate=1e-3)
model.compile(loss="sparse_categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"])
model.fit(train_set, epochs=20, validation_data=valid_set)
Epoch 1/20
98/Unknown 28s 223ms/step - accuracy: 0.3137 - loss: 2.6649
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
[[{{node IteratorGetNext}}]]
/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.
self.gen.throw(value)
98/98 ━━━━━━━━━━━━━━━━━━━━ 32s 260ms/step - accuracy: 0.3156 - loss: 2.6569 - val_accuracy: 0.0786 - val_loss: 3.7352 Epoch 2/20
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
[[{{node IteratorGetNext}}]]
98/98 ━━━━━━━━━━━━━━━━━━━━ 25s 254ms/step - accuracy: 0.7535 - loss: 0.9438 - val_accuracy: 0.1163 - val_loss: 3.4637 Epoch 3/20
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
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98/98 ━━━━━━━━━━━━━━━━━━━━ 25s 254ms/step - accuracy: 0.7917 - loss: 0.7477 - val_accuracy: 0.1727 - val_loss: 3.1308 Epoch 4/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 26s 264ms/step - accuracy: 0.8093 - loss: 0.6566 - val_accuracy: 0.2591 - val_loss: 2.5800 Epoch 5/20
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
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98/98 ━━━━━━━━━━━━━━━━━━━━ 25s 254ms/step - accuracy: 0.8241 - loss: 0.5948 - val_accuracy: 0.5689 - val_loss: 1.4124 Epoch 6/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 25s 258ms/step - accuracy: 0.8339 - loss: 0.5554 - val_accuracy: 0.7331 - val_loss: 0.8900 Epoch 7/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 25s 260ms/step - accuracy: 0.8438 - loss: 0.5161 - val_accuracy: 0.7983 - val_loss: 0.6799 Epoch 8/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 26s 262ms/step - accuracy: 0.8522 - loss: 0.4868 - val_accuracy: 0.8114 - val_loss: 0.6398 Epoch 9/20
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
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98/98 ━━━━━━━━━━━━━━━━━━━━ 25s 259ms/step - accuracy: 0.8592 - loss: 0.4599 - val_accuracy: 0.8212 - val_loss: 0.6050 Epoch 10/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 25s 254ms/step - accuracy: 0.8688 - loss: 0.4258 - val_accuracy: 0.8226 - val_loss: 0.6068 Epoch 11/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 25s 260ms/step - accuracy: 0.8758 - loss: 0.4037 - val_accuracy: 0.8244 - val_loss: 0.6088 Epoch 12/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 26s 261ms/step - accuracy: 0.8813 - loss: 0.3820 - val_accuracy: 0.8236 - val_loss: 0.6096 Epoch 13/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 26s 261ms/step - accuracy: 0.8891 - loss: 0.3574 - val_accuracy: 0.8212 - val_loss: 0.6176 Epoch 14/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 26s 261ms/step - accuracy: 0.8954 - loss: 0.3381 - val_accuracy: 0.8231 - val_loss: 0.6219 Epoch 15/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 26s 262ms/step - accuracy: 0.9038 - loss: 0.3131 - val_accuracy: 0.8211 - val_loss: 0.6362 Epoch 16/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 26s 262ms/step - accuracy: 0.9090 - loss: 0.2961 - val_accuracy: 0.8198 - val_loss: 0.6408 Epoch 17/20
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
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98/98 ━━━━━━━━━━━━━━━━━━━━ 25s 254ms/step - accuracy: 0.9140 - loss: 0.2782 - val_accuracy: 0.8175 - val_loss: 0.6539 Epoch 18/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 25s 256ms/step - accuracy: 0.9198 - loss: 0.2611 - val_accuracy: 0.8178 - val_loss: 0.6626 Epoch 19/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 25s 258ms/step - accuracy: 0.9250 - loss: 0.2443 - val_accuracy: 0.8115 - val_loss: 0.6911 Epoch 20/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 25s 259ms/step - accuracy: 0.9269 - loss: 0.2380 - val_accuracy: 0.8130 - val_loss: 0.6963
<keras.src.callbacks.history.History at 0x7f2409f96ab0>
model.evaluate(test_set)
34/34 ━━━━━━━━━━━━━━━━━━━━ 3s 92ms/step - accuracy: 0.8088 - loss: 0.7133
[0.7032589316368103, 0.8107198476791382]
def generate_chorale(model, seed_chords, length, temperature=1):
arpegio = preprocess(tf.constant(seed_chords, dtype=tf.int64))
arpegio = tf.reshape(arpegio, [1, -1])
for chord in range(length):
for note in range(4):
next_note_probas = model.predict(arpegio)[0, -1:]
rescaled_logits = tf.math.log(next_note_probas) / temperature
next_note = tf.random.categorical(rescaled_logits, num_samples=1)
arpegio = tf.concat([arpegio, next_note], axis=1)
arpegio = tf.where(arpegio == 0, arpegio, arpegio + min_note - 1)
return tf.reshape(arpegio, shape=[-1, 4])
import numpy as np
from IPython.display import Audio
def notes_to_frequencies(notes):
# Frequency doubles when you go up one octave; there are 12 semi-tones
# per octave; Note A on octave 4 is 440 Hz, and it is note number 69.
return 2 ** ((np.array(notes) - 69) / 12) * 440
def frequencies_to_samples(frequencies, tempo, sample_rate):
note_duration = 60 / tempo # the tempo is measured in beats per minutes
# To reduce click sound at every beat, we round the frequencies to try to
# get the samples close to zero at the end of each note.
frequencies = (note_duration * frequencies).round() / note_duration
n_samples = int(note_duration * sample_rate)
time = np.linspace(0, note_duration, n_samples)
sine_waves = np.sin(2 * np.pi * frequencies.reshape(-1, 1) * time)
# Removing all notes with frequencies ≤ 9 Hz (includes note 0 = silence)
sine_waves *= (frequencies > 9.).reshape(-1, 1)
return sine_waves.reshape(-1)
def chords_to_samples(chords, tempo, sample_rate):
freqs = notes_to_frequencies(chords)
freqs = np.r_[freqs, freqs[-1:]] # make last note a bit longer
merged = np.mean([frequencies_to_samples(melody, tempo, sample_rate)
for melody in freqs.T], axis=0)
n_fade_out_samples = sample_rate * 60 // tempo # fade out last note
fade_out = np.linspace(1., 0., n_fade_out_samples)**2
merged[-n_fade_out_samples:] *= fade_out
return merged
def play_chords(chords, tempo=160, amplitude=0.1, sample_rate=44100, filepath=None):
samples = amplitude * chords_to_samples(chords, tempo, sample_rate)
if filepath:
from scipy.io import wavfile
samples = (2**15 * samples).astype(np.int16)
wavfile.write(filepath, sample_rate, samples)
return display(Audio(filepath))
else:
return display(Audio(samples, rate=sample_rate))
seed_chords = test_chorales[3][:8]
new_chorale_v2_cold = generate_chorale(model, seed_chords, 56, temperature=1.5)
play_chords(new_chorale_v2_cold, filepath="bach.wav")
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 421ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 479ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 43ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step 1/1 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