my code:
# %%
import os
import numpy as np
import pandas as pd
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.layers import Input, Dropout
from tensorflow.keras import Model

# %%
def data_generator(file_paths, batch_size, train=True, scaler=None):
    while True:  # Loop forever so the generator never terminates
        # Shuffle file paths to ensure random distribution of data across files
        np.random.shuffle(file_paths)
        
        for file_path in file_paths:
            data = pd.read_csv(file_path)

            # Split features and labels
            X = data.drop([
                'Date', 'Symbol',
                'y_High_1d', 'y_Low_1d', 'y_Priority_1d',
                 'y_High_2d', 'y_Low_2d', 'y_Priority_2d',
                  'y_High_3d', 'y_Low_3d', 'y_Priority_3d',
                   'y_High_5d', 'y_Low_5d', 'y_Priority_5d'], axis=1).values

            y_high_1d = data['y_High_1d'].values
            y_low_1d = data['y_Low_1d'].values
            y_priority_1d = data['y_Priority_1d'].values

            y_high_2d = data['y_High_2d'].values
            y_low_2d = data['y_Low_2d'].values
            y_priority_2d = data['y_Priority_2d'].values

            y_high_3d = data['y_High_3d'].values
            y_low_3d = data['y_Low_3d'].values
            y_priority_3d = data['y_Priority_3d'].values

            y_high_5d = data['y_High_5d'].values
            y_low_5d = data['y_Low_5d'].values
            y_priority_5d = data['y_Priority_5d'].values
            
            # Optionally, apply preprocessing here (e.g., Scaler transform)
            if scaler is not None and train:
                X = scaler.fit_transform(X)
            elif scaler is not None:
                X = scaler.transform(X)
                
            # Splitting data into batches
            for i in range(0, len(data), batch_size):
                end = i + batch_size
                X_batch = X[i:end]

                y_high_batch_1d = y_high_1d[i:end]
                y_low_batch_1d = y_low_1d[i:end]
                y_priority_batch_1d = y_priority_1d[i:end]

                y_high_batch_2d = y_high_2d[i:end]
                y_low_batch_2d = y_low_2d[i:end]
                y_priority_batch_2d = y_priority_2d[i:end]

                y_high_batch_3d = y_high_3d[i:end]
                y_low_batch_3d = y_low_3d[i:end]
                y_priority_batch_3d = y_priority_3d[i:end]

                y_high_batch_5d = y_high_5d[i:end]
                y_low_batch_5d = y_low_5d[i:end]
                y_priority_batch_5d = y_priority_5d[i:end]
                
                yield X_batch, [y_high_batch_1d, y_low_batch_1d,y_priority_batch_1d,
                y_high_batch_2d, y_low_batch_2d,y_priority_batch_2d,
                y_high_batch_3d, y_low_batch_3d, y_priority_batch_3d,
                y_high_batch_5d, y_low_batch_5d, y_priority_batch_5d]

# %%
dataset_dir = r'C:\Users\arisa\Desktop\day_spot'
file_paths = [os.path.join(dataset_dir, file_name) for file_name in os.listdir(dataset_dir)]
train_files, val_files = train_test_split(file_paths, test_size=0.06, random_state=42)

batch_size = 72

# %%
# Optional: Initialize a scaler for data normalization
scaler = StandardScaler()

# %%
# Create data generators
train_gen = data_generator(train_files, batch_size, train=True, scaler=scaler)
val_gen = data_generator(val_files, batch_size, train=False, scaler=scaler)


# %%
input_shape = (72, 6427)

inputs = Input(shape=input_shape)  # Corrected the input shape specification

x = Dense(6427, activation='relu')(inputs)
x = Dropout(0.25) (x)
x = Dense(3200, activation='relu') (x)
x = Dropout(0.20) (x)
x = Dense(1800, activation='relu') (x)
x = Dropout(0.15) (x)
x = Dense(1024, activation='relu') (x)
x = Dropout(0.10) (x)
x = Dense(512, activation='relu') (x)
x = Dropout(0.05) (x)
x = Dense(256, activation='relu') (x)
x = Dense(128, activation='relu') (x)
x = Dense(64, activation='relu') (x)
x = Dense(32, activation='relu')  (x)



# Defining three separate outputs

out_high_1d = Dense(1, name='high_output_1d')(x)  # No activation, linear output
out_low_1d = Dense(1, name='low_output_1d')(x)    # No activation, linear output
out_priority_1d = Dense(1, activation='sigmoid', name='priority_output_1d')(x)

out_high_2d = Dense(1, name='high_output_2d')(x)  # No activation, linear output
out_low_2d = Dense(1, name='low_output_2d')(x)    # No activation, linear output
out_priority_2d = Dense(1, activation='sigmoid', name='priority_output_2d')(x)

out_high_3d = Dense(1, name='high_output_3d')(x)  # No activation, linear output
out_low_3d = Dense(1, name='low_output_3d')(x)    # No activation, linear output
out_priority_3d = Dense(1, activation='sigmoid', name='priority_output_3d')(x)

out_high_5d = Dense(1, name='high_output_5d')(x)  # No activation, linear output
out_low_5d = Dense(1, name='low_output_5d')(x)    # No activation, linear output
out_priority_5d = Dense(1, activation='sigmoid', name='priority_output_5d')(x)

# Constructing the model
model = Model(inputs=inputs, outputs=[
    out_high_1d, out_low_1d, out_priority_1d,out_high_2d, out_low_2d, out_priority_2d,
    out_high_3d, out_low_3d, out_priority_3d,out_high_5d, out_low_5d, out_priority_5d])

model.compile(optimizer='adam',
              loss={
                'high_output_1d': 'mse', 'low_output_1d': 'mse', 'priority_output_1d': 'binary_crossentropy',
                'high_output_2d': 'mse', 'low_output_2d': 'mse', 'priority_output_2d': 'binary_crossentropy',
                'high_output_3d': 'mse', 'low_output_3d': 'mse', 'priority_output_3d': 'binary_crossentropy',
                'high_output_5d': 'mse', 'low_output_5d': 'mse', 'priority_output_5d': 'binary_crossentropy'
              },
              metrics={
                'high_output_1d': ['mae'], 'low_output_1d': ['mae'], 'priority_output_1d': ['accuracy'],
                'high_output_2d': ['mae'], 'low_output_2d': ['mae'], 'priority_output_2d': ['accuracy'],
                'high_output_3d': ['mae'], 'low_output_3d': ['mae'], 'priority_output_3d': ['accuracy'],
                'high_output_5d': ['mae'], 'low_output_5d': ['mae'], 'priority_output_5d': ['accuracy']
              },
              loss_weights={
                'high_output_1d': 1.0, 'low_output_1d': 1.0, 'priority_output_1d': 1.0,
                'high_output_2d': 1.0, 'low_output_2d': 1.0, 'priority_output_2d': 1.0,
                'high_output_3d': 1.0, 'low_output_3d': 1.0, 'priority_output_3d': 1.0,
                'high_output_5d': 1.0, 'low_output_5d': 1.0, 'priority_output_5d': 1.0
              }
              )

# %%
import os
import pandas as pd

def count_samples(file_paths):
    total_samples = 0
    for file_path in file_paths:
        df = pd.read_csv(file_path)
        samples = len(df)
        total_samples += samples
    return total_samples

number_of_training_samples = count_samples(train_files)
number_of_validation_samples = count_samples(val_files)

print(f'Number of training samples: {number_of_training_samples}')
print(f'Number of validation samples: {number_of_validation_samples}')

# %%
import tensorflow as tf
output_signature = (
    tf.TensorSpec(shape=(None, X_batch.shape[1]), dtype=tf.float32),  # for X_batch
    (
        tf.TensorSpec(shape=(None, 1), dtype=tf.float32),  # For each y_batch
        tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
        tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
        tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
        tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
        tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
        tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
        tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
        tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
        tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
        tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
        tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
    )
)

train_dataset = tf.data.Dataset.from_generator(
    lambda: train_gen,
    output_signature=output_signature
)

val_dataset = tf.data.Dataset.from_generator(
    lambda: val_gen,
    output_signature=output_signature
)

# %%

# You'll need to determine steps_per_epoch and validation_steps based on your data size and batch size
steps_per_epoch = np.ceil(number_of_training_samples / batch_size).astype(int)  # Replace number_of_training_samples
validation_steps = np.ceil(number_of_validation_samples / batch_size).astype(int)  # Replace number_of_validation_samples

# model.fit(train_gen,
#           steps_per_epoch=steps_per_epoch,
#           validation_data=val_gen,
#           validation_steps=validation_steps,
#           epochs=1000)

# Then use these datasets in your model.fit call
model.fit(train_dataset,
          steps_per_epoch=steps_per_epoch,
          validation_data=val_dataset,
          validation_steps=validation_steps,
          epochs=1000)




error:
{
	"name": "InvalidArgumentError",
	"message": "Graph execution error:

Detected at node PyFunc defined at (most recent call last):
<stack traces unavailable>
TypeError: `generator` yielded an element that did not match the expected structure. The expected structure was (tf.float32, (tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32)), but the yielded element was (array([[-0.39491499, -0.40790701, -0.37860123, ...,  0.        ,
         0.        ,  0.        ],
       [-0.387636  , -0.40270658, -0.36623326, ...,  0.        ,
         0.        ,  0.        ],
       [-0.37878316, -0.3817255 , -0.36090224, ...,  0.        ,
         0.        ,  0.        ],
       ...,
       [-0.52475656, -0.52626182, -0.50974435, ...,  0.        ,
         0.        ,  0.        ],
       [-0.51747756, -0.52410992, -0.52168584, ...,  0.        ,
         0.        ,  0.        ],
       [-0.5349865 , -0.54096651, -0.52360501, ...,  0.        ,
         0.        ,  0.        ]]), [array([ 1.88098495,  4.27753452,  0.06548788,  2.0242915 , -0.1001001 ,
        0.30160858,  0.48143054,  3.14661134,  0.27275827,  1.09270356,
        1.1967617 ,  1.36746143,  4.2057699 ,  0.94754653,  1.02739726,
        0.44688897,  0.57430007,  4.045605  ,  0.        ,  1.76322418,
       -0.03586801,  4.12411626,  0.07616146,  1.39458573,  1.54723127,
        0.        ,  1.61366872,  2.4262607 ,  3.27255727,  2.55102041,
        2.03117619,  2.6119403 ,  3.37806571,  0.36798528,  2.41134752,
        3.83536015,  4.19296664,  3.51339482,  5.09009009, 14.82608696,
        3.75567478,  0.29154519,  3.20901995,  0.04295533,  3.21543408,
        5.70910739,  2.98313878,  1.13350126,  3.37781485,  3.61201299,
        1.90555095,  3.76229158,  1.95295162,  0.65387969,  1.0521701 ,
        3.44827586,  0.92470277,  1.36503743,  6.97368421,  4.68364832,
        2.41014799,  0.3776752 ,  4.12684622,  1.22466216,  1.92560175,
        2.47131509,  4.88021295,  1.13636364,  2.78276481,  1.63355408,
        1.51724138,  6.37413395]), array([ -1.19699042,  -1.85247558,  -4.78061559,  -0.94466937,
        -2.002002  ,  -3.58579088,  -1.89133425,  -0.82987552,
        -4.90964882,  -2.99612266,  -0.80957409,  -0.56100982,
        -0.66041015,  -2.43654822,  -0.99315068,  -5.15641114,
        -3.51758794,  -1.25045973,  -2.2411953 ,  -1.11550918,
       -11.94404591,  -0.90337785,  -7.99695354,  -1.64068909,
        -1.79153094, -22.43485342,  -1.99335548,  -3.04471931,
        -0.46750818,  -4.63821892,  -2.50354275,  -2.51865672,
        -0.78667284,  -3.31186753,  -0.80378251,  -1.87090739,
        -3.69702435,  -3.20597277,  -1.66666667,  -1.69565217,
        -0.99050764,  -5.91420242,  -0.56374675,  -9.66494845,
        -0.68902159,  -3.94200272,  -0.51880674,  -3.61041142,
        -0.54211843,  -2.5974026 ,  -4.97100249,  -3.8477982 ,
        -1.42032845,  -2.83347864,  -2.41122315,  -3.26914465,
        -1.58520476,  -1.58520476,  -1.27192982,  -6.03944125,
        -1.18393235,  -4.1963911 ,  -1.39009557,  -5.40540541,
        -1.48796499,  -2.33892321,  -0.88731145,  -3.71503497,
        -0.53859964,  -4.63576159,  -1.10344828,  -1.61662818]), array([0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0,
       1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1,
       0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
       0, 1, 0, 1, 1, 0], dtype=int64), array([ 5.88235294,  4.27753452,  0.06548788,  2.0242915 , -0.1001001 ,
        0.30160858,  2.57909216,  3.14661134,  0.27275827,  1.33944307,
        1.75994368,  5.11921459,  4.2057699 ,  0.94754653,  1.02739726,
        0.44688897,  1.54343144,  4.045605  ,  0.60476699,  1.76322418,
       -0.03586801,  4.12411626,  0.07616146,  2.29696473,  1.54723127,
        0.        ,  2.18319886,  5.0903901 ,  3.36605891,  2.55102041,
        3.92064242,  4.19776119,  3.37806571,  0.36798528,  4.96453901,
        8.09167446,  6.26690712,  3.51339482, 18.96396396, 14.82608696,
        3.75567478,  0.29154519,  3.20901995,  0.04295533,  7.16582453,
        7.92931581,  4.15045396,  4.07220823,  6.46371977,  3.61201299,
        1.90555095,  3.76229158,  2.48557479,  0.65387969,  1.27137221,
        3.44827586,  1.36503743,  7.39762219, 11.75438596,  4.68364832,
        2.41014799,  0.58749475,  4.12684622,  1.22466216,  1.92560175,
        4.32480141,  4.88021295,  1.13636364,  3.32136445,  1.63355408,
        5.88505747,  6.37413395]), array([ -1.19699042,  -2.05456383,  -4.78061559,  -0.94466937,
        -4.004004  ,  -4.39008043,  -1.89133425,  -3.5615491 ,
        -6.17115581,  -2.99612266,  -0.80957409,  -0.56100982,
        -0.66041015,  -2.43654822,  -5.51369863,  -7.59711241,
        -3.6252692 ,  -1.25045973,  -2.2411953 , -11.6588701 ,
       -11.94404591,  -5.1060487 ,  -8.6824067 ,  -1.64068909,
       -22.43485342, -22.43485342,  -3.27479829,  -3.04471931,
        -3.88031791,  -4.63821892,  -2.50354275,  -2.51865672,
        -2.73021749,  -3.49586017,  -0.80378251,  -1.87090739,
        -3.69702435,  -4.12823891,  -1.66666667,  -1.69565217,
        -6.76846884,  -5.91420242,  -8.80312229,  -9.66494845,
        -2.61828204,  -3.94200272,  -0.73497622,  -3.61041142,
        -0.54211843,  -6.89935065,  -6.83512842,  -5.04489098,
        -1.42032845,  -2.96425458,  -5.30469093,  -3.26914465,
        -1.58520476,  -1.58520476,  -1.27192982,  -6.03944125,
        -3.46723044,  -4.74192195,  -2.69331017,  -5.40540541,
        -3.15098468,  -2.33892321,  -2.26264419,  -3.71503497,
        -3.05206463,  -5.03311258,  -2.06896552,  -1.61662818]), array([0., 1., 1., 0., 1., 1., 0., 1., 1., 0., 0., 0., 0., 1., 1., 1., 0.,
       0., 0., 1., 1., 1., 1., 0., 1., 1., 0., 0., 1., 1., 0., 0., 1., 1.,
       0., 0., 0., 1., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 1., 1.,
       1., 0., 1., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 1., 1.,
       1., 1., 0., 0.]), array([ 5.88235294,  4.27753452,  0.06548788,  2.0242915 , -0.1001001 ,
        0.30160858,  2.57909216,  3.14661134,  0.27275827,  1.9034191 ,
        5.52622316,  5.11921459,  4.2057699 ,  0.94754653,  1.02739726,
        0.44688897,  1.54343144,  4.045605  ,  0.60476699,  1.76322418,
       -0.03586801,  4.12411626,  0.07616146,  2.29696473,  1.54723127,
        0.        ,  4.84100617,  5.18553758,  3.36605891,  2.55102041,
        5.52668871,  4.19776119,  3.37806571,  2.11591536,  9.26713948,
       10.24321796,  6.26690712, 15.98594642, 18.96396396, 14.82608696,
        3.75567478,  0.29154519,  3.20901995,  0.21477663,  9.41662839,
        9.15269597,  7.17682663,  7.17884131,  6.46371977,  3.61201299,
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        4.32480141,  4.88021295,  1.13636364,  3.32136445,  1.67770419,
        5.88505747,  6.37413395]), array([ -1.19699042,  -2.05456383,  -4.78061559,  -2.93522267,
        -4.8048048 ,  -4.39008043,  -4.09215956,  -4.84094053,
        -6.17115581,  -2.99612266,  -0.80957409,  -0.56100982,
        -0.66041015,  -6.63282572,  -7.94520548,  -7.70024063,
        -3.6252692 ,  -1.25045973, -12.66453219, -11.6588701 ,
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       -22.43485342, -22.43485342,  -3.27479829,  -3.04471931,
        -3.88031791,  -4.63821892,  -2.50354275,  -2.51865672,
        -2.91531698,  -3.49586017,  -0.80378251,  -1.87090739,
        -3.69702435,  -4.12823891,  -1.66666667,  -1.7826087 ,
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        -2.61828204,  -3.94200272,  -0.73497622,  -3.61041142,
        -4.33694746,  -8.72564935,  -7.995029  ,  -5.04489098,
        -1.42032845,  -5.8413252 ,  -5.30469093,  -3.26914465,
        -1.58520476,  -1.58520476,  -1.27192982,  -6.20377979,
        -4.01691332,  -6.00083928,  -2.69331017,  -6.54560811,
        -3.15098468,  -2.78022948,  -2.26264419,  -5.59440559,
        -3.45601436,  -5.9602649 ,  -2.06896552,  -1.61662818]), array([0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 1., 1., 1., 0.,
       0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 0., 0., 1., 0.,
       0., 0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 1., 1., 1.,
       1., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 0., 1., 1., 1.,
       1., 0., 0., 0.]), array([ 5.88235294,  4.27753452,  0.06548788,  2.0242915 , -0.1001001 ,
        0.30160858,  2.57909216,  3.14661134,  2.21616093,  5.67500881,
        5.52622316,  5.11921459,  4.2057699 ,  0.94754653,  1.02739726,
        0.44688897,  1.54343144,  4.045605  ,  0.60476699,  1.76322418,
       -0.03586801,  4.12411626,  0.07616146,  2.29696473,  1.54723127,
        0.        ,  4.93592786,  5.18553758,  4.44132772,  3.61781076,
        5.52668871,  4.19776119,  6.94123091,  8.41766329, 11.44208038,
       23.52666043, 19.07123535, 15.98594642, 18.96396396, 14.82608696,
        3.75567478,  0.29154519,  3.29575022,  3.47938144, 13.87230133,
       15.67739012, 10.37613489,  7.17884131,  6.46371977,  3.61201299,
        1.90555095,  3.76229158,  2.52996005,  0.69747167,  6.9267865 ,
       14.10658307, 12.19726993, 12.19726993, 11.75438596,  4.68364832,
        2.41014799,  0.58749475,  4.12684622,  1.22466216,  3.45733042,
        4.32480141,  4.88021295,  1.13636364,  3.36624776,  1.67770419,
        5.88505747,  6.37413395]), array([ -1.19699042,  -3.09868643,  -6.58153242,  -3.74493927,
        -6.94027361,  -7.77479893,  -5.36451169,  -4.84094053,
        -6.17115581,  -2.99612266,  -0.80957409,  -3.26086957,
        -6.56934307,  -9.13705584,  -8.04794521,  -7.70024063,
       -11.88083274,  -9.709452  , -14.05193881, -13.70996761,
       -13.98852224, -25.17674784, -27.45620716, -21.86218212,
       -22.43485342, -22.43485342,  -3.27479829,  -3.04471931,
        -3.88031791,  -4.63821892,  -2.50354275,  -2.51865672,
        -2.91531698,  -3.49586017,  -0.80378251,  -1.87090739,
        -3.69702435,  -4.12823891,  -1.66666667,  -8.56521739,
       -13.20676847, -12.41149521,  -8.80312229,  -9.66494845,
        -2.61828204,  -3.94200272,  -0.82144401,  -5.58354324,
        -7.38115096,  -9.86201299,  -7.995029  ,  -7.6528431 ,
        -4.12782956,  -5.8413252 ,  -5.30469093,  -3.26914465,
        -1.58520476,  -1.58520476,  -1.27192982,  -7.97041906,
        -5.28541226,  -7.13386488,  -3.8662033 ,  -6.96790541,
        -3.58862144,  -4.67784643,  -4.56965395,  -6.90559441,
        -4.39856373,  -5.9602649 ,  -2.06896552,  -1.61662818]), array([0., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 1., 1., 1., 1., 1.,
       1., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       0., 0., 0., 0.])]).
Traceback (most recent call last):

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\data\\ops\\from_generator_op.py\", line 204, in generator_py_func
    flattened_values = nest.flatten_up_to(output_types, values)

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\data\\util\
est.py\", line 237, in flatten_up_to
    return nest_util.flatten_up_to(

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\util\
est_util.py\", line 1541, in flatten_up_to
    return _tf_data_flatten_up_to(shallow_tree, input_tree)

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\util\
est_util.py\", line 1570, in _tf_data_flatten_up_to
    _tf_data_assert_shallow_structure(shallow_tree, input_tree)

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\util\
est_util.py\", line 1444, in _tf_data_assert_shallow_structure
    _tf_data_assert_shallow_structure(

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\util\
est_util.py\", line 1414, in _tf_data_assert_shallow_structure
    raise TypeError(

TypeError: If shallow structure is a sequence, input must also be a sequence. Input has type: 'list'.


The above exception was the direct cause of the following exception:


Traceback (most recent call last):

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\ops\\script_ops.py\", line 270, in __call__
    ret = func(*args)

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\autograph\\impl\\api.py\", line 643, in wrapper
    return func(*args, **kwargs)

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\data\\ops\\from_generator_op.py\", line 206, in generator_py_func
    raise TypeError(

TypeError: `generator` yielded an element that did not match the expected structure. The expected structure was (tf.float32, (tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32)), but the yielded element was (array([[-0.39491499, -0.40790701, -0.37860123, ...,  0.        ,
         0.        ,  0.        ],
       [-0.387636  , -0.40270658, -0.36623326, ...,  0.        ,
         0.        ,  0.        ],
       [-0.37878316, -0.3817255 , -0.36090224, ...,  0.        ,
         0.        ,  0.        ],
       ...,
       [-0.52475656, -0.52626182, -0.50974435, ...,  0.        ,
         0.        ,  0.        ],
       [-0.51747756, -0.52410992, -0.52168584, ...,  0.        ,
         0.        ,  0.        ],
       [-0.5349865 , -0.54096651, -0.52360501, ...,  0.        ,
         0.        ,  0.        ]]), [array([ 1.88098495,  4.27753452,  0.06548788,  2.0242915 , -0.1001001 ,
        0.30160858,  0.48143054,  3.14661134,  0.27275827,  1.09270356,
        1.1967617 ,  1.36746143,  4.2057699 ,  0.94754653,  1.02739726,
        0.44688897,  0.57430007,  4.045605  ,  0.        ,  1.76322418,
       -0.03586801,  4.12411626,  0.07616146,  1.39458573,  1.54723127,
        0.        ,  1.61366872,  2.4262607 ,  3.27255727,  2.55102041,
        2.03117619,  2.6119403 ,  3.37806571,  0.36798528,  2.41134752,
        3.83536015,  4.19296664,  3.51339482,  5.09009009, 14.82608696,
        3.75567478,  0.29154519,  3.20901995,  0.04295533,  3.21543408,
        5.70910739,  2.98313878,  1.13350126,  3.37781485,  3.61201299,
        1.90555095,  3.76229158,  1.95295162,  0.65387969,  1.0521701 ,
        3.44827586,  0.92470277,  1.36503743,  6.97368421,  4.68364832,
        2.41014799,  0.3776752 ,  4.12684622,  1.22466216,  1.92560175,
        2.47131509,  4.88021295,  1.13636364,  2.78276481,  1.63355408,
        1.51724138,  6.37413395]), array([ -1.19699042,  -1.85247558,  -4.78061559,  -0.94466937,
        -2.002002  ,  -3.58579088,  -1.89133425,  -0.82987552,
        -4.90964882,  -2.99612266,  -0.80957409,  -0.56100982,
        -0.66041015,  -2.43654822,  -0.99315068,  -5.15641114,
        -3.51758794,  -1.25045973,  -2.2411953 ,  -1.11550918,
       -11.94404591,  -0.90337785,  -7.99695354,  -1.64068909,
        -1.79153094, -22.43485342,  -1.99335548,  -3.04471931,
        -0.46750818,  -4.63821892,  -2.50354275,  -2.51865672,
        -0.78667284,  -3.31186753,  -0.80378251,  -1.87090739,
        -3.69702435,  -3.20597277,  -1.66666667,  -1.69565217,
        -0.99050764,  -5.91420242,  -0.56374675,  -9.66494845,
        -0.68902159,  -3.94200272,  -0.51880674,  -3.61041142,
        -0.54211843,  -2.5974026 ,  -4.97100249,  -3.8477982 ,
        -1.42032845,  -2.83347864,  -2.41122315,  -3.26914465,
        -1.58520476,  -1.58520476,  -1.27192982,  -6.03944125,
        -1.18393235,  -4.1963911 ,  -1.39009557,  -5.40540541,
        -1.48796499,  -2.33892321,  -0.88731145,  -3.71503497,
        -0.53859964,  -4.63576159,  -1.10344828,  -1.61662818]), array([0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0,
       1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1,
       0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
       0, 1, 0, 1, 1, 0], dtype=int64), array([ 5.88235294,  4.27753452,  0.06548788,  2.0242915 , -0.1001001 ,
        0.30160858,  2.57909216,  3.14661134,  0.27275827,  1.33944307,
        1.75994368,  5.11921459,  4.2057699 ,  0.94754653,  1.02739726,
        0.44688897,  1.54343144,  4.045605  ,  0.60476699,  1.76322418,
       -0.03586801,  4.12411626,  0.07616146,  2.29696473,  1.54723127,
        0.        ,  2.18319886,  5.0903901 ,  3.36605891,  2.55102041,
        3.92064242,  4.19776119,  3.37806571,  0.36798528,  4.96453901,
        8.09167446,  6.26690712,  3.51339482, 18.96396396, 14.82608696,
        3.75567478,  0.29154519,  3.20901995,  0.04295533,  7.16582453,
        7.92931581,  4.15045396,  4.07220823,  6.46371977,  3.61201299,
        1.90555095,  3.76229158,  2.48557479,  0.65387969,  1.27137221,
        3.44827586,  1.36503743,  7.39762219, 11.75438596,  4.68364832,
        2.41014799,  0.58749475,  4.12684622,  1.22466216,  1.92560175,
        4.32480141,  4.88021295,  1.13636364,  3.32136445,  1.63355408,
        5.88505747,  6.37413395]), array([ -1.19699042,  -2.05456383,  -4.78061559,  -0.94466937,
        -4.004004  ,  -4.39008043,  -1.89133425,  -3.5615491 ,
        -6.17115581,  -2.99612266,  -0.80957409,  -0.56100982,
        -0.66041015,  -2.43654822,  -5.51369863,  -7.59711241,
        -3.6252692 ,  -1.25045973,  -2.2411953 , -11.6588701 ,
       -11.94404591,  -5.1060487 ,  -8.6824067 ,  -1.64068909,
       -22.43485342, -22.43485342,  -3.27479829,  -3.04471931,
        -3.88031791,  -4.63821892,  -2.50354275,  -2.51865672,
        -2.73021749,  -3.49586017,  -0.80378251,  -1.87090739,
        -3.69702435,  -4.12823891,  -1.66666667,  -1.69565217,
        -6.76846884,  -5.91420242,  -8.80312229,  -9.66494845,
        -2.61828204,  -3.94200272,  -0.73497622,  -3.61041142,
        -0.54211843,  -6.89935065,  -6.83512842,  -5.04489098,
        -1.42032845,  -2.96425458,  -5.30469093,  -3.26914465,
        -1.58520476,  -1.58520476,  -1.27192982,  -6.03944125,
        -3.46723044,  -4.74192195,  -2.69331017,  -5.40540541,
        -3.15098468,  -2.33892321,  -2.26264419,  -3.71503497,
        -3.05206463,  -5.03311258,  -2.06896552,  -1.61662818]), array([0., 1., 1., 0., 1., 1., 0., 1., 1., 0., 0., 0., 0., 1., 1., 1., 0.,
       0., 0., 1., 1., 1., 1., 0., 1., 1., 0., 0., 1., 1., 0., 0., 1., 1.,
       0., 0., 0., 1., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 1., 1.,
       1., 0., 1., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 1., 1.,
       1., 1., 0., 0.]), array([ 5.88235294,  4.27753452,  0.06548788,  2.0242915 , -0.1001001 ,
        0.30160858,  2.57909216,  3.14661134,  0.27275827,  1.9034191 ,
        5.52622316,  5.11921459,  4.2057699 ,  0.94754653,  1.02739726,
        0.44688897,  1.54343144,  4.045605  ,  0.60476699,  1.76322418,
       -0.03586801,  4.12411626,  0.07616146,  2.29696473,  1.54723127,
        0.        ,  4.84100617,  5.18553758,  3.36605891,  2.55102041,
        5.52668871,  4.19776119,  3.37806571,  2.11591536,  9.26713948,
       10.24321796,  6.26690712, 15.98594642, 18.96396396, 14.82608696,
        3.75567478,  0.29154519,  3.20901995,  0.21477663,  9.41662839,
        9.15269597,  7.17682663,  7.17884131,  6.46371977,  3.61201299,
        1.90555095,  3.76229158,  2.48557479,  0.69747167,  1.27137221,
        3.44827586,  7.39762219, 12.19726993, 11.75438596,  4.68364832,
        2.41014799,  0.58749475,  4.12684622,  1.22466216,  3.45733042,
        4.32480141,  4.88021295,  1.13636364,  3.32136445,  1.67770419,
        5.88505747,  6.37413395]), array([ -1.19699042,  -2.05456383,  -4.78061559,  -2.93522267,
        -4.8048048 ,  -4.39008043,  -4.09215956,  -4.84094053,
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        -3.6252692 ,  -1.25045973, -12.66453219, -11.6588701 ,
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       -22.43485342, -22.43485342,  -3.27479829,  -3.04471931,
        -3.88031791,  -4.63821892,  -2.50354275,  -2.51865672,
        -2.91531698,  -3.49586017,  -0.80378251,  -1.87090739,
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        -4.33694746,  -8.72564935,  -7.995029  ,  -5.04489098,
        -1.42032845,  -5.8413252 ,  -5.30469093,  -3.26914465,
        -1.58520476,  -1.58520476,  -1.27192982,  -6.20377979,
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        -3.15098468,  -2.78022948,  -2.26264419,  -5.59440559,
        -3.45601436,  -5.9602649 ,  -2.06896552,  -1.61662818]), array([0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 1., 1., 1., 0.,
       0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 0., 0., 1., 0.,
       0., 0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 1., 1., 1.,
       1., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 0., 1., 1., 1.,
       1., 0., 0., 0.]), array([ 5.88235294,  4.27753452,  0.06548788,  2.0242915 , -0.1001001 ,
        0.30160858,  2.57909216,  3.14661134,  2.21616093,  5.67500881,
        5.52622316,  5.11921459,  4.2057699 ,  0.94754653,  1.02739726,
        0.44688897,  1.54343144,  4.045605  ,  0.60476699,  1.76322418,
       -0.03586801,  4.12411626,  0.07616146,  2.29696473,  1.54723127,
        0.        ,  4.93592786,  5.18553758,  4.44132772,  3.61781076,
        5.52668871,  4.19776119,  6.94123091,  8.41766329, 11.44208038,
       23.52666043, 19.07123535, 15.98594642, 18.96396396, 14.82608696,
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       15.67739012, 10.37613489,  7.17884131,  6.46371977,  3.61201299,
        1.90555095,  3.76229158,  2.52996005,  0.69747167,  6.9267865 ,
       14.10658307, 12.19726993, 12.19726993, 11.75438596,  4.68364832,
        2.41014799,  0.58749475,  4.12684622,  1.22466216,  3.45733042,
        4.32480141,  4.88021295,  1.13636364,  3.36624776,  1.67770419,
        5.88505747,  6.37413395]), array([ -1.19699042,  -3.09868643,  -6.58153242,  -3.74493927,
        -6.94027361,  -7.77479893,  -5.36451169,  -4.84094053,
        -6.17115581,  -2.99612266,  -0.80957409,  -3.26086957,
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        -2.61828204,  -3.94200272,  -0.82144401,  -5.58354324,
        -7.38115096,  -9.86201299,  -7.995029  ,  -7.6528431 ,
        -4.12782956,  -5.8413252 ,  -5.30469093,  -3.26914465,
        -1.58520476,  -1.58520476,  -1.27192982,  -7.97041906,
        -5.28541226,  -7.13386488,  -3.8662033 ,  -6.96790541,
        -3.58862144,  -4.67784643,  -4.56965395,  -6.90559441,
        -4.39856373,  -5.9602649 ,  -2.06896552,  -1.61662818]), array([0., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 1., 1., 1., 1., 1.,
       1., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       0., 0., 0., 0.])]).


\t [[{{node PyFunc}}]]
\t [[IteratorGetNext]] [Op:__inference_one_step_on_iterator_7596]",
	"stack": "---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
Cell In[31], line 12
      3 validation_steps = np.ceil(number_of_validation_samples / batch_size).astype(int)  # Replace number_of_validation_samples
      5 # model.fit(train_gen,
      6 #           steps_per_epoch=steps_per_epoch,
      7 #           validation_data=val_gen,
   (...)
     10 
     11 # Then use these datasets in your model.fit call
---> 12 model.fit(train_dataset,
     13           steps_per_epoch=steps_per_epoch,
     14           validation_data=val_dataset,
     15           validation_steps=validation_steps,
     16           epochs=1000)

File c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)
    119     filtered_tb = _process_traceback_frames(e.__traceback__)
    120     # To get the full stack trace, call:
    121     # `keras.config.disable_traceback_filtering()`
--> 122     raise e.with_traceback(filtered_tb) from None
    123 finally:
    124     del filtered_tb

File c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     51 try:
     52   ctx.ensure_initialized()
---> 53   tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
     54                                       inputs, attrs, num_outputs)
     55 except core._NotOkStatusException as e:
     56   if name is not None:

InvalidArgumentError: Graph execution error:

Detected at node PyFunc defined at (most recent call last):
<stack traces unavailable>
TypeError: `generator` yielded an element that did not match the expected structure. The expected structure was (tf.float32, (tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32)), but the yielded element was (array([[-0.39491499, -0.40790701, -0.37860123, ...,  0.        ,
         0.        ,  0.        ],
       [-0.387636  , -0.40270658, -0.36623326, ...,  0.        ,
         0.        ,  0.        ],
       [-0.37878316, -0.3817255 , -0.36090224, ...,  0.        ,
         0.        ,  0.        ],
       ...,
       [-0.52475656, -0.52626182, -0.50974435, ...,  0.        ,
         0.        ,  0.        ],
       [-0.51747756, -0.52410992, -0.52168584, ...,  0.        ,
         0.        ,  0.        ],
       [-0.5349865 , -0.54096651, -0.52360501, ...,  0.        ,
         0.        ,  0.        ]]), [array([ 1.88098495,  4.27753452,  0.06548788,  2.0242915 , -0.1001001 ,
        0.30160858,  0.48143054,  3.14661134,  0.27275827,  1.09270356,
        1.1967617 ,  1.36746143,  4.2057699 ,  0.94754653,  1.02739726,
        0.44688897,  0.57430007,  4.045605  ,  0.        ,  1.76322418,
       -0.03586801,  4.12411626,  0.07616146,  1.39458573,  1.54723127,
        0.        ,  1.61366872,  2.4262607 ,  3.27255727,  2.55102041,
        2.03117619,  2.6119403 ,  3.37806571,  0.36798528,  2.41134752,
        3.83536015,  4.19296664,  3.51339482,  5.09009009, 14.82608696,
        3.75567478,  0.29154519,  3.20901995,  0.04295533,  3.21543408,
        5.70910739,  2.98313878,  1.13350126,  3.37781485,  3.61201299,
        1.90555095,  3.76229158,  1.95295162,  0.65387969,  1.0521701 ,
        3.44827586,  0.92470277,  1.36503743,  6.97368421,  4.68364832,
        2.41014799,  0.3776752 ,  4.12684622,  1.22466216,  1.92560175,
        2.47131509,  4.88021295,  1.13636364,  2.78276481,  1.63355408,
        1.51724138,  6.37413395]), array([ -1.19699042,  -1.85247558,  -4.78061559,  -0.94466937,
        -2.002002  ,  -3.58579088,  -1.89133425,  -0.82987552,
        -4.90964882,  -2.99612266,  -0.80957409,  -0.56100982,
        -0.66041015,  -2.43654822,  -0.99315068,  -5.15641114,
        -3.51758794,  -1.25045973,  -2.2411953 ,  -1.11550918,
       -11.94404591,  -0.90337785,  -7.99695354,  -1.64068909,
        -1.79153094, -22.43485342,  -1.99335548,  -3.04471931,
        -0.46750818,  -4.63821892,  -2.50354275,  -2.51865672,
        -0.78667284,  -3.31186753,  -0.80378251,  -1.87090739,
        -3.69702435,  -3.20597277,  -1.66666667,  -1.69565217,
        -0.99050764,  -5.91420242,  -0.56374675,  -9.66494845,
        -0.68902159,  -3.94200272,  -0.51880674,  -3.61041142,
        -0.54211843,  -2.5974026 ,  -4.97100249,  -3.8477982 ,
        -1.42032845,  -2.83347864,  -2.41122315,  -3.26914465,
        -1.58520476,  -1.58520476,  -1.27192982,  -6.03944125,
        -1.18393235,  -4.1963911 ,  -1.39009557,  -5.40540541,
        -1.48796499,  -2.33892321,  -0.88731145,  -3.71503497,
        -0.53859964,  -4.63576159,  -1.10344828,  -1.61662818]), array([0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0,
       1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1,
       0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
       0, 1, 0, 1, 1, 0], dtype=int64), array([ 5.88235294,  4.27753452,  0.06548788,  2.0242915 , -0.1001001 ,
        0.30160858,  2.57909216,  3.14661134,  0.27275827,  1.33944307,
        1.75994368,  5.11921459,  4.2057699 ,  0.94754653,  1.02739726,
        0.44688897,  1.54343144,  4.045605  ,  0.60476699,  1.76322418,
       -0.03586801,  4.12411626,  0.07616146,  2.29696473,  1.54723127,
        0.        ,  2.18319886,  5.0903901 ,  3.36605891,  2.55102041,
        3.92064242,  4.19776119,  3.37806571,  0.36798528,  4.96453901,
        8.09167446,  6.26690712,  3.51339482, 18.96396396, 14.82608696,
        3.75567478,  0.29154519,  3.20901995,  0.04295533,  7.16582453,
        7.92931581,  4.15045396,  4.07220823,  6.46371977,  3.61201299,
        1.90555095,  3.76229158,  2.48557479,  0.65387969,  1.27137221,
        3.44827586,  1.36503743,  7.39762219, 11.75438596,  4.68364832,
        2.41014799,  0.58749475,  4.12684622,  1.22466216,  1.92560175,
        4.32480141,  4.88021295,  1.13636364,  3.32136445,  1.63355408,
        5.88505747,  6.37413395]), array([ -1.19699042,  -2.05456383,  -4.78061559,  -0.94466937,
        -4.004004  ,  -4.39008043,  -1.89133425,  -3.5615491 ,
        -6.17115581,  -2.99612266,  -0.80957409,  -0.56100982,
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       -22.43485342, -22.43485342,  -3.27479829,  -3.04471931,
        -3.88031791,  -4.63821892,  -2.50354275,  -2.51865672,
        -2.73021749,  -3.49586017,  -0.80378251,  -1.87090739,
        -3.69702435,  -4.12823891,  -1.66666667,  -1.69565217,
        -6.76846884,  -5.91420242,  -8.80312229,  -9.66494845,
        -2.61828204,  -3.94200272,  -0.73497622,  -3.61041142,
        -0.54211843,  -6.89935065,  -6.83512842,  -5.04489098,
        -1.42032845,  -2.96425458,  -5.30469093,  -3.26914465,
        -1.58520476,  -1.58520476,  -1.27192982,  -6.03944125,
        -3.46723044,  -4.74192195,  -2.69331017,  -5.40540541,
        -3.15098468,  -2.33892321,  -2.26264419,  -3.71503497,
        -3.05206463,  -5.03311258,  -2.06896552,  -1.61662818]), array([0., 1., 1., 0., 1., 1., 0., 1., 1., 0., 0., 0., 0., 1., 1., 1., 0.,
       0., 0., 1., 1., 1., 1., 0., 1., 1., 0., 0., 1., 1., 0., 0., 1., 1.,
       0., 0., 0., 1., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 1., 1.,
       1., 0., 1., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 1., 1.,
       1., 1., 0., 0.]), array([ 5.88235294,  4.27753452,  0.06548788,  2.0242915 , -0.1001001 ,
        0.30160858,  2.57909216,  3.14661134,  0.27275827,  1.9034191 ,
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        4.32480141,  4.88021295,  1.13636364,  3.32136445,  1.67770419,
        5.88505747,  6.37413395]), array([ -1.19699042,  -2.05456383,  -4.78061559,  -2.93522267,
        -4.8048048 ,  -4.39008043,  -4.09215956,  -4.84094053,
        -6.17115581,  -2.99612266,  -0.80957409,  -0.56100982,
        -0.66041015,  -6.63282572,  -7.94520548,  -7.70024063,
        -3.6252692 ,  -1.25045973, -12.66453219, -11.6588701 ,
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        -3.88031791,  -4.63821892,  -2.50354275,  -2.51865672,
        -2.91531698,  -3.49586017,  -0.80378251,  -1.87090739,
        -3.69702435,  -4.12823891,  -1.66666667,  -1.7826087 ,
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        -2.61828204,  -3.94200272,  -0.73497622,  -3.61041142,
        -4.33694746,  -8.72564935,  -7.995029  ,  -5.04489098,
        -1.42032845,  -5.8413252 ,  -5.30469093,  -3.26914465,
        -1.58520476,  -1.58520476,  -1.27192982,  -6.20377979,
        -4.01691332,  -6.00083928,  -2.69331017,  -6.54560811,
        -3.15098468,  -2.78022948,  -2.26264419,  -5.59440559,
        -3.45601436,  -5.9602649 ,  -2.06896552,  -1.61662818]), array([0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 1., 1., 1., 0.,
       0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 0., 0., 1., 0.,
       0., 0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 1., 1., 1.,
       1., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 0., 1., 1., 1.,
       1., 0., 0., 0.]), array([ 5.88235294,  4.27753452,  0.06548788,  2.0242915 , -0.1001001 ,
        0.30160858,  2.57909216,  3.14661134,  2.21616093,  5.67500881,
        5.52622316,  5.11921459,  4.2057699 ,  0.94754653,  1.02739726,
        0.44688897,  1.54343144,  4.045605  ,  0.60476699,  1.76322418,
       -0.03586801,  4.12411626,  0.07616146,  2.29696473,  1.54723127,
        0.        ,  4.93592786,  5.18553758,  4.44132772,  3.61781076,
        5.52668871,  4.19776119,  6.94123091,  8.41766329, 11.44208038,
       23.52666043, 19.07123535, 15.98594642, 18.96396396, 14.82608696,
        3.75567478,  0.29154519,  3.29575022,  3.47938144, 13.87230133,
       15.67739012, 10.37613489,  7.17884131,  6.46371977,  3.61201299,
        1.90555095,  3.76229158,  2.52996005,  0.69747167,  6.9267865 ,
       14.10658307, 12.19726993, 12.19726993, 11.75438596,  4.68364832,
        2.41014799,  0.58749475,  4.12684622,  1.22466216,  3.45733042,
        4.32480141,  4.88021295,  1.13636364,  3.36624776,  1.67770419,
        5.88505747,  6.37413395]), array([ -1.19699042,  -3.09868643,  -6.58153242,  -3.74493927,
        -6.94027361,  -7.77479893,  -5.36451169,  -4.84094053,
        -6.17115581,  -2.99612266,  -0.80957409,  -3.26086957,
        -6.56934307,  -9.13705584,  -8.04794521,  -7.70024063,
       -11.88083274,  -9.709452  , -14.05193881, -13.70996761,
       -13.98852224, -25.17674784, -27.45620716, -21.86218212,
       -22.43485342, -22.43485342,  -3.27479829,  -3.04471931,
        -3.88031791,  -4.63821892,  -2.50354275,  -2.51865672,
        -2.91531698,  -3.49586017,  -0.80378251,  -1.87090739,
        -3.69702435,  -4.12823891,  -1.66666667,  -8.56521739,
       -13.20676847, -12.41149521,  -8.80312229,  -9.66494845,
        -2.61828204,  -3.94200272,  -0.82144401,  -5.58354324,
        -7.38115096,  -9.86201299,  -7.995029  ,  -7.6528431 ,
        -4.12782956,  -5.8413252 ,  -5.30469093,  -3.26914465,
        -1.58520476,  -1.58520476,  -1.27192982,  -7.97041906,
        -5.28541226,  -7.13386488,  -3.8662033 ,  -6.96790541,
        -3.58862144,  -4.67784643,  -4.56965395,  -6.90559441,
        -4.39856373,  -5.9602649 ,  -2.06896552,  -1.61662818]), array([0., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 1., 1., 1., 1., 1.,
       1., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       0., 0., 0., 0.])]).
Traceback (most recent call last):

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\data\\ops\\from_generator_op.py\", line 204, in generator_py_func
    flattened_values = nest.flatten_up_to(output_types, values)

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\data\\util\
est.py\", line 237, in flatten_up_to
    return nest_util.flatten_up_to(

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\util\
est_util.py\", line 1541, in flatten_up_to
    return _tf_data_flatten_up_to(shallow_tree, input_tree)

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\util\
est_util.py\", line 1570, in _tf_data_flatten_up_to
    _tf_data_assert_shallow_structure(shallow_tree, input_tree)

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\util\
est_util.py\", line 1444, in _tf_data_assert_shallow_structure
    _tf_data_assert_shallow_structure(

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\util\
est_util.py\", line 1414, in _tf_data_assert_shallow_structure
    raise TypeError(

TypeError: If shallow structure is a sequence, input must also be a sequence. Input has type: 'list'.


The above exception was the direct cause of the following exception:


Traceback (most recent call last):

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\ops\\script_ops.py\", line 270, in __call__
    ret = func(*args)

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\autograph\\impl\\api.py\", line 643, in wrapper
    return func(*args, **kwargs)

  File \"c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\data\\ops\\from_generator_op.py\", line 206, in generator_py_func
    raise TypeError(

TypeError: `generator` yielded an element that did not match the expected structure. The expected structure was (tf.float32, (tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32)), but the yielded element was (array([[-0.39491499, -0.40790701, -0.37860123, ...,  0.        ,
         0.        ,  0.        ],
       [-0.387636  , -0.40270658, -0.36623326, ...,  0.        ,
         0.        ,  0.        ],
       [-0.37878316, -0.3817255 , -0.36090224, ...,  0.        ,
         0.        ,  0.        ],
       ...,
       [-0.52475656, -0.52626182, -0.50974435, ...,  0.        ,
         0.        ,  0.        ],
       [-0.51747756, -0.52410992, -0.52168584, ...,  0.        ,
         0.        ,  0.        ],
       [-0.5349865 , -0.54096651, -0.52360501, ...,  0.        ,
         0.        ,  0.        ]]), [array([ 1.88098495,  4.27753452,  0.06548788,  2.0242915 , -0.1001001 ,
        0.30160858,  0.48143054,  3.14661134,  0.27275827,  1.09270356,
        1.1967617 ,  1.36746143,  4.2057699 ,  0.94754653,  1.02739726,
        0.44688897,  0.57430007,  4.045605  ,  0.        ,  1.76322418,
       -0.03586801,  4.12411626,  0.07616146,  1.39458573,  1.54723127,
        0.        ,  1.61366872,  2.4262607 ,  3.27255727,  2.55102041,
        2.03117619,  2.6119403 ,  3.37806571,  0.36798528,  2.41134752,
        3.83536015,  4.19296664,  3.51339482,  5.09009009, 14.82608696,
        3.75567478,  0.29154519,  3.20901995,  0.04295533,  3.21543408,
        5.70910739,  2.98313878,  1.13350126,  3.37781485,  3.61201299,
        1.90555095,  3.76229158,  1.95295162,  0.65387969,  1.0521701 ,
        3.44827586,  0.92470277,  1.36503743,  6.97368421,  4.68364832,
        2.41014799,  0.3776752 ,  4.12684622,  1.22466216,  1.92560175,
        2.47131509,  4.88021295,  1.13636364,  2.78276481,  1.63355408,
        1.51724138,  6.37413395]), array([ -1.19699042,  -1.85247558,  -4.78061559,  -0.94466937,
        -2.002002  ,  -3.58579088,  -1.89133425,  -0.82987552,
        -4.90964882,  -2.99612266,  -0.80957409,  -0.56100982,
        -0.66041015,  -2.43654822,  -0.99315068,  -5.15641114,
        -3.51758794,  -1.25045973,  -2.2411953 ,  -1.11550918,
       -11.94404591,  -0.90337785,  -7.99695354,  -1.64068909,
        -1.79153094, -22.43485342,  -1.99335548,  -3.04471931,
        -0.46750818,  -4.63821892,  -2.50354275,  -2.51865672,
        -0.78667284,  -3.31186753,  -0.80378251,  -1.87090739,
        -3.69702435,  -3.20597277,  -1.66666667,  -1.69565217,
        -0.99050764,  -5.91420242,  -0.56374675,  -9.66494845,
        -0.68902159,  -3.94200272,  -0.51880674,  -3.61041142,
        -0.54211843,  -2.5974026 ,  -4.97100249,  -3.8477982 ,
        -1.42032845,  -2.83347864,  -2.41122315,  -3.26914465,
        -1.58520476,  -1.58520476,  -1.27192982,  -6.03944125,
        -1.18393235,  -4.1963911 ,  -1.39009557,  -5.40540541,
        -1.48796499,  -2.33892321,  -0.88731145,  -3.71503497,
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\t [[{{node PyFunc}}]]
\t [[IteratorGetNext]] [Op:__inference_one_step_on_iterator_7596]"
}