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"ValueError: Layer 'dense' expected 1 input(s). Received 2 instead" on tf.keras.models.load_model #63853

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AaronEggert opened this issue Mar 17, 2024 · 18 comments
Open

"ValueError: Layer 'dense' expected 1 input(s). Received 2 instead" on tf.keras.models.load_model #63853

AaronEggert opened this issue Mar 17, 2024 · 18 comments
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@Venkat6871
Labels
comp:model Model related issues subtype:windows Windows Build/Installation Issues TF 2.16 type:bug Bug

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@AaronEggert
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Issue type

Bug

Have you reproduced the bug with TensorFlow Nightly?

No

Source

source

TensorFlow version

v2.16.0-rc0-18-g5bc9d26649c 2.16.1

Custom code

Yes

OS platform and distribution

Windows 11 (KB5035853)

Mobile device

No response

Python version

3.11.8

Bazel version

No response

GCC/compiler version

No response

CUDA/cuDNN version

No response

GPU model and memory

No response

Current behavior?

I want to load a saved model directly after training. But when it executes tf.keras.models.load_model it says ValueError: Layer 'dense' expected 1 input(s). Received 2 instead.

Standalone code to reproduce the issue

base_model = tf.keras.applications.ResNet50(include_top=False, weights='imagenet')
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
output_layer = tf.keras.layers.Dense(1)

model = tf.keras.Sequential([
    base_model,
    global_average_layer,
    output_layer
])


model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

history = model.fit(train_dataset,
                    epochs=epochs,
                    validation_data=val_dataset)

loss, accuracy = model.evaluate(val_dataset)

# Save the model
model.save('model-v1.keras')

model.summary()

n_model = tf.keras.models.load_model('model-v1.keras')

n_model.summary()

Relevant log output

Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ resnet50 (Functional)                │ (None, 12, 12, 2048)        │      23,587,712 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ global_average_pooling2d             │ (None, 2048)                │               0 │
│ (GlobalAveragePooling2D)             │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense (Dense)                        │ (None, 1)                   │           2,049 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 70,663,045 (269.56 MB)
 Trainable params: 23,536,641 (89.79 MB)
 Non-trainable params: 53,120 (207.50 KB)
 Optimizer params: 47,073,284 (179.57 MB)
Traceback (most recent call last):
  File "C:\Users\Aaron-Desktop\OneDrive\Dokumente\Meine Projekte\Deep Learning\Hund erkennung\code\train.py", line 63, in <module>
    n_model = tf.keras.models.load_model('model-v1.keras')
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\saving\saving_api.py", line 176, in load_model
    return saving_lib.load_model(
           ^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\saving\saving_lib.py", line 155, in load_model
    model = deserialize_keras_object(
            ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\saving\serialization_lib.py", line 711, in deserialize_keras_object
    instance = cls.from_config(inner_config)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\models\sequential.py", line 336, in from_config
    model.add(layer)
  File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\models\sequential.py", line 117, in add
    self._maybe_rebuild()
  File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\models\sequential.py", line 136, in _maybe_rebuild
    self.build(input_shape)
  File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\layers\layer.py", line 224, in build_wrapper
    original_build_method(*args, **kwargs)
  File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\models\sequential.py", line 177, in build
    x = layer(x)
        ^^^^^^^^
  File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\utils\traceback_utils.py", line 123, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\layers\input_spec.py", line 156, in assert_input_compatibility
    raise ValueError(
ValueError: Layer 'dense' expected 1 input(s). Received 2 instead.
@google-ml-butler google-ml-butler bot added the type:bug Bug label Mar 17, 2024
@Venkat6871 Venkat6871 added subtype:windows Windows Build/Installation Issues TF 2.16 comp:model Model related issues labels Mar 21, 2024
@Venkat6871
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Hi @AaronEggert ,
Sorry for the delay, This error typically occurs when the input shape of the loaded model does not match the expected input shape of the layers.
Here i am providing gist with updated code for your reference. Please go through it once.

Thank you!

@Venkat6871 Venkat6871 added the stat:awaiting response Status - Awaiting response from author label Mar 21, 2024
@AaronEggert
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Author

Thank you for your help.
When I define the input size while loading the model, I get the same Error.

input_shape = (224, 224, 3)
model = tf.keras.models.load_model('model-v1.keras', compile=False, custom_objects={'input_shape': input_shape})

Traceback (most recent call last):
File "C:\Users\Aaron-Desktop\OneDrive\Dokumente\Meine Projekte\Deep Learning\Hund erkennung\code\train.py", line 64, in
n_model = tf.keras.models.load_model('model-v1.keras', compile=False, custom_objects={'input_shape': input_shape})
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\saving\saving_api.py", line 176, in load_model
return saving_lib.load_model(
^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\saving\saving_lib.py", line 155, in load_model
model = deserialize_keras_object(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\saving\serialization_lib.py", line 711, in deserialize_keras_object
instance = cls.from_config(inner_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\models\sequential.py", line 336, in from_config
model.add(layer)
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\models\sequential.py", line 117, in add
self._maybe_rebuild()
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\models\sequential.py", line 136, in _maybe_rebuild
self.build(input_shape)
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\layers\layer.py", line 224, in build_wrapper
original_build_method(*args, **kwargs)
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\models\sequential.py", line 177, in build
x = layer(x)
^^^^^^^^
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\utils\traceback_utils.py", line 123, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\layers\input_spec.py", line 156, in assert_input_compatibility
raise ValueError(
ValueError: Layer 'dense' expected 1 input(s). Received 2 instead.

@google-ml-butler google-ml-butler bot removed the stat:awaiting response Status - Awaiting response from author label Mar 21, 2024
@gustavozf
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I have the exact same error. Although the platform is different (MacBook Pro w/ M3), the TF version is the same (2.16.1). My models are also really similar to the ones described here (CNN Backbone + GlobalAveragePooling2D + Dense). The proposed fix also did not seem to work properly.

Do we have any updates on this issue?

@AaronEggert
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Author

I found a solution to that:
You need to define the input_size on loading the model

This Code whould help you:

input_shape = (224, 224, 3)
model = tf.keras.models.load_model('model.keras', compile=False, custom_objects={'input_shape': input_shape})

@google-ml-butler Google-ML-Butler
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Are you satisfied with the resolution of your issue?
Yes
No

@gustavozf
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Well, it doesn't seem to work properly. Am I missing something?

>>> tf.keras.models.load_model('model.keras', compile=False, custom_objects={'input_shape': (48, 48, 3)})
...
ValueError: Layer 'dense' expected 1 input(s). Received 2 instead.

@AaronEggert AaronEggert reopened this Mar 25, 2024
@H0TB0X420
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I am having the shape issue on a Windows 10 machine with TF 2.16.1 and similar model. The proposed solution yields the same ValueError as before.

@sergvbash
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Have exactly the same issue on Mac. Setting the input shape does't help

@squallssck
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squallssck commented Mar 28, 2024 •
edited

same here with tf 2.16.1， I've also tried tf2.15 with keras 3.0&3.1.1 but with no luck. My trained and saved .keras model （trained with 2.16.1）is not able to load with keras.saving.load_model()

base_model = keras.applications.MobileNetV2(input_shape=(224,224,3),include_top=False,weights="imagenet")
model = keras.Sequential([
    base_model,
    keras.layers.GlobalAveragePooling2D(),
    keras.layers.Dropout(rate=0.5),
    keras.layers.Dense(8,activation='softmax'),
])

model.compile(...)
model.fit(...)

model.summary() #OK

model.save("test_mobilenetv2_save.keras")

model_2 = keras.saving.load_model("test_mobilenetv2_save.keras") #Error
model_2 = keras.saving.load_model("test_mobilenetv2_save.keras",compile=False, custom_objects={'input_shape': input_shape}) #Error

model_2.summary()

a walkround is to save the trained model with .weights.h5 and build model , load weights(but you need to retrain the model and save weights only which is sometimes not acceptable)

model.save_weights("mobilenetv2_weights_only.weights.h5")

then


base_model2 = keras.applications.MobileNetV2(input_shape=(224,224,3),include_top=False,weights="imagenet")

base_model2.trainable = True

model2 = keras.Sequential([
    base_model2,
    keras.layers.GlobalAveragePooling2D(),
    keras.layers.Dropout(rate=0.5),
    keras.layers.Dense(8,activation='softmax'),
])

model2.compile(
    loss=loss,
    # optimizer=keras.optimizers.SGD(learning_rate=0.01),
    optimizer = optimizer,
    metrics=[
        metrics.CategoricalAccuracy(),
        metrics.TopKCategoricalAccuracy(k=3),
    ]
)

model2.summary()

model2.predict(np.ones((32,224,224,3))) #must build model first or you not able to load weights

model2.load_weights("mobilenetv2_weights_only.weights.h5", skip_mismatch=False)

@gustavozf
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@squallssck I think that we reached the same workaround. The only thing working for now is saving the weights and loading them to the model:

model = build_model()
model.build(input_shape=(None, *INPUT_SHAPE))
model.load_weights('model.weights.h5')

Not the optimal solution, but it works.

@91Abdullah
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Any update on this issue?

@Rebelioner
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есть ли фид бек по тому когда эта ошибка появилась , потому что раньше все работало отлично и никаких проблем не наблюдалось

@91Abdullah
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Version 2.15.0.post1 is working fine for *.keras extensions. The model should also be trained on this version. It's working fine on 2.15.0.post1

@ba049
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ba049 commented Apr 4, 2024

Thanks @91Abdullah. It worked for me!

@XXTongC
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XXTongC commented Apr 7, 2024

I am also having the shape issue on a Ubuntu 20.4 machine with TF 2.16.1
this how i trained my model, here is when i load my model happens

import pathlib
import pickle
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
from keras.preprocessing.image import ImageDataGenerator

'''
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))
'''
format_1 = '/kaggle/input/flower-dataset/train'
format_2 = '/kaggle/input/flower-dataset/valid'
train_dir = pathlib.Path(format_1)
valid_dir = pathlib.Path(format_2)
train_count = len(list(train_dir.glob('*/*.jpg')))
valid_count = len(list(valid_dir.glob('*/*.jpg')))

print(train_count)
print(valid_count)

CLASS_NAMES = np.array([item.name for item in train_dir.glob('*') if item.is_dir()])

image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255)
BATCH_SIZE = 32
IMG_HEIGHT = 224
IMG_WIDTH = 224
train_data_gen = image_generator.flow_from_directory(directory=str(train_dir),
                                                     batch_size=BATCH_SIZE,
                                                     target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                     shuffle=True,
                                                     classes=list(CLASS_NAMES))
valid_data_gen = image_generator.flow_from_directory(directory=str(valid_dir),
                                                     batch_size=BATCH_SIZE,
                                                     target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                     shuffle=True,
                                                     classes=list(CLASS_NAMES))
print(CLASS_NAMES)
ResNet50 = tf.keras.applications.resnet_v2.ResNet50V2(include_top=False, weights='imagenet', input_shape=(224, 224, 3))
ResNet50.trainable = True
my_net = tf.keras.models.Sequential()
my_net.add(ResNet50)
my_net.add(tf.keras.layers.GlobalAveragePooling2D())
my_net.add(tf.keras.layers.Dense(100, activation='softmax'))
my_net.summary()
my_net.compile(optimizer=tf.keras.optimizers.Adamax(0.001),
            loss='categorical_crossentropy',
            metrics=['accuracy'])
epoch_steps = train_count // BATCH_SIZE
val_steps = valid_count // BATCH_SIZE
my_net.fit_generator(
    train_data_gen,
    steps_per_epoch=epoch_steps,
    epochs=5,
    validation_data=valid_data_gen,
    validation_steps=val_steps
)
my_net.save('flower.h5')

here is error

Traceback (most recent call last):
  File "/root/keras-flask-deploy-webapp/app.py", line 42, in <module>
    model = tf.keras.models.load_model(MODEL_PATH, compile=False, custom_objects={'input_shape': input_shape})
  File "/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py", line 183, in load_model
    return legacy_h5_format.load_model_from_hdf5(filepath)
  File "/usr/local/lib/python3.10/dist-packages/keras/src/legacy/saving/legacy_h5_format.py", line 133, in load_model_from_hdf5
    model = saving_utils.model_from_config(
  File "/usr/local/lib/python3.10/dist-packages/keras/src/legacy/saving/saving_utils.py", line 85, in model_from_config
    return serialization.deserialize_keras_object(
  File "/usr/local/lib/python3.10/dist-packages/keras/src/legacy/saving/serialization.py", line 495, in deserialize_keras_object
    deserialized_obj = cls.from_config(
  File "/usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py", line 335, in from_config
    model.add(layer)
  File "/usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py", line 116, in add
    self._maybe_rebuild()
  File "/usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py", line 135, in _maybe_rebuild
    self.build(input_shape)
  File "/usr/local/lib/python3.10/dist-packages/keras/src/layers/layer.py", line 223, in build_wrapper
    original_build_method(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py", line 176, in build
    x = layer(x)
  File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 122, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/usr/local/lib/python3.10/dist-packages/keras/src/layers/input_spec.py", line 156, in assert_input_compatibility
    raise ValueError(
ValueError: Layer 'dense' expected 1 input(s). Received 2 instead.

@gmtanner-cord
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Also having the same issue. Windows 10 machine with TF 2.16.1. Here is how I trained my model:

import tensorflow as tf
from tensorflow.keras.applications import EfficientNetV2S

V2S_model = EfficientNetV2S(weights='imagenet',
                        include_top=False,
                        input_shape=(224, 224, 3))

for layer in V2S_model.layers:
  layer.trainable = False
from tensorflow.keras import layers

image_preprocess = tf.keras.Sequential([
    tf.keras.Input((None,None,3)),
    layers.Resizing(224,224, crop_to_aspect_ratio = True),
    layers.RandomFlip("horizontal_and_vertical"),
    layers.RandomRotation(0.2)
], name = "image_aug")

transfer_model = tf.keras.Sequential([
    tf.keras.Input((None,None,3)),
    image_preprocess,
    V2S_model,
    layers.GlobalAveragePooling2D(),
    layers.Dense(512, activation='relu'),
    layers.Dropout(0.2),
    layers.Dense(1, activation = 'sigmoid')
])

transfer_model.summary()

metrics = [tf.keras.metrics.BinaryAccuracy(threshold=0.5, name='accuracy'),
           tf.keras.metrics.AUC(),
           tf.keras.metrics.TruePositives(),
           tf.keras.metrics.TrueNegatives(),
           tf.keras.metrics.FalsePositives(),
           tf.keras.metrics.FalseNegatives()
          ]

transfer_model.compile(loss=tf.keras.losses.BinaryCrossentropy(),
                       optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
                       metrics=metrics)

BATCH_SIZE = 16
IMAGE_SIZE = (224,224)
SEED = 42

# This sets up a training and validation set from our ../data/ directory
train_dataset = tf.keras.utils.image_dataset_from_directory(
    '../data/',
    class_names = ['not_niblet','niblet'],
    color_mode='rgb',
    batch_size=BATCH_SIZE,
    image_size=IMAGE_SIZE,
    shuffle=True,
    validation_split=0.2,
    subset='training',
    seed=SEED)

# This is the validation set. Notice `shuffle = FALSE` and `subset = validation`
val_dataset = tf.keras.utils.image_dataset_from_directory(
    '../data/',
    class_names = ['not_niblet','niblet'],
    color_mode='rgb',
    batch_size=BATCH_SIZE,
    image_size=IMAGE_SIZE,
    shuffle=True,
    validation_split=0.2,
    subset='validation',
    seed=SEED)

from tensorflow.keras.callbacks import EarlyStopping

es = EarlyStopping(patience=15, monitor='val_loss')

history = transfer_model.fit(train_dataset, epochs=100,
                             validation_data=val_dataset,
                             callbacks=[es])

transfer_model.save('../models/transfer_model_gt_2024_04_23.keras')

transfer_model_loaded = tf.keras.models.load_model('../models/transfer_model_gt_2024_04_23.keras')

and here is my error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[16], line 2
      1 input_shape = (None,None,3)
----> 2 transfer_model_loaded = tf.keras.models.load_model('../models/transfer_model_gt_2024_04_23.keras', compile=False, custom_objects={'input_shape': input_shape})

File [~\anaconda3\envs\CS380_final\Lib\site-packages\keras\src\saving\saving_api.py:176](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/saving/saving_api.py#line=175), in load_model(filepath, custom_objects, compile, safe_mode)
    173         is_keras_zip = True
    175 if is_keras_zip:
--> 176     return saving_lib.load_model(
    177         filepath,
    178         custom_objects=custom_objects,
    179         compile=compile,
    180         safe_mode=safe_mode,
    181     )
    182 if str(filepath).endswith((".h5", ".hdf5")):
    183     return legacy_h5_format.load_model_from_hdf5(filepath)

File [~\anaconda3\envs\CS380_final\Lib\site-packages\keras\src\saving\saving_lib.py:152](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/saving/saving_lib.py#line=151), in load_model(filepath, custom_objects, compile, safe_mode)
    147     raise ValueError(
    148         "Invalid filename: expected a `.keras` extension. "
    149         f"Received: filepath={filepath}"
    150     )
    151 with open(filepath, "rb") as f:
--> 152     return _load_model_from_fileobj(
    153         f, custom_objects, compile, safe_mode
    154     )

File [~\anaconda3\envs\CS380_final\Lib\site-packages\keras\src\saving\saving_lib.py:170](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/saving/saving_lib.py#line=169), in _load_model_from_fileobj(fileobj, custom_objects, compile, safe_mode)
    168 # Construct the model from the configuration file in the archive.
    169 with ObjectSharingScope():
--> 170     model = deserialize_keras_object(
    171         config_dict, custom_objects, safe_mode=safe_mode
    172     )
    174 all_filenames = zf.namelist()
    175 if _VARS_FNAME + ".h5" in all_filenames:

File [~\anaconda3\envs\CS380_final\Lib\site-packages\keras\src\saving\serialization_lib.py:711](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/saving/serialization_lib.py#line=710), in deserialize_keras_object(config, custom_objects, safe_mode, **kwargs)
    709 with custom_obj_scope, safe_mode_scope:
    710     try:
--> 711         instance = cls.from_config(inner_config)
    712     except TypeError as e:
    713         raise TypeError(
    714             f"{cls} could not be deserialized properly. Please"
    715             " ensure that components that are Python object"
   (...)
    719             f"\n\nconfig={config}[.\n\nException](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/n/nException) encountered: {e}"
    720         )

File [~\anaconda3\envs\CS380_final\Lib\site-packages\keras\src\models\sequential.py:339](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/models/sequential.py#line=338), in Sequential.from_config(cls, config, custom_objects)
    334     else:
    335         layer = serialization_lib.deserialize_keras_object(
    336             layer_config,
    337             custom_objects=custom_objects,
    338         )
--> 339     model.add(layer)
    340 if (
    341     not model._functional
    342     and build_input_shape
    343     and isinstance(build_input_shape, (tuple, list))
    344 ):
    345     model.build(build_input_shape)

File [~\anaconda3\envs\CS380_final\Lib\site-packages\keras\src\models\sequential.py:120](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/models/sequential.py#line=119), in Sequential.add(self, layer, rebuild)
    118 self._layers.append(layer)
    119 if rebuild:
--> 120     self._maybe_rebuild()
    121 else:
    122     self.built = False

File [~\anaconda3\envs\CS380_final\Lib\site-packages\keras\src\models\sequential.py:139](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/models/sequential.py#line=138), in Sequential._maybe_rebuild(self)
    137 if isinstance(self._layers[0], InputLayer) and len(self._layers) > 1:
    138     input_shape = self._layers[0].batch_shape
--> 139     self.build(input_shape)

File [~\anaconda3\envs\CS380_final\Lib\site-packages\keras\src\layers\layer.py:222](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/layers/layer.py#line=221), in Layer.__new__.<locals>.build_wrapper(*args, **kwargs)
    219 @wraps(original_build_method)
    220 def build_wrapper(*args, **kwargs):
    221     with obj._open_name_scope():
--> 222         original_build_method(*args, **kwargs)
    223     # Record build config.
    224     signature = inspect.signature(original_build_method)

File [~\anaconda3\envs\CS380_final\Lib\site-packages\keras\src\models\sequential.py:180](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/models/sequential.py#line=179), in Sequential.build(self, input_shape)
    178 for layer in self._layers[1:]:
    179     try:
--> 180         x = layer(x)
    181     except NotImplementedError:
    182         # Can happen if shape inference is not implemented.
    183         # TODO: consider reverting inbound nodes on layers processed.
    184         return

File [~\anaconda3\envs\CS380_final\Lib\site-packages\keras\src\utils\traceback_utils.py:122](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/utils/traceback_utils.py#line=121), 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 [~\anaconda3\envs\CS380_final\Lib\site-packages\keras\src\layers\input_spec.py:156](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/layers/input_spec.py#line=155), in assert_input_compatibility(input_spec, inputs, layer_name)
    154 inputs = tree.flatten(inputs)
    155 if len(input_spec) != len(inputs):
--> 156     raise ValueError(
    157         f"Layer '{layer_name}' expected {len(input_spec)} input(s). "
    158         f"Received {len(inputs)} instead."
    159     )
    160 for x in inputs:
    161     # Having a shape/dtype is the only commonality of the various
    162     # tensor-like objects that may be passed. The most common kind of
    163     # invalid type we are guarding for is a Layer instance (Functional API),
    164     # which does not have a `shape` attribute.
    165     if not hasattr(x, "shape"):

ValueError: Layer 'dense' expected 1 input(s). Received 2 instead.

@91Abdullah
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@gmtanner-cord try to use version 2.15 it should work as expected.

@XXTongC
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XXTongC commented Apr 24, 2024 via email

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