Addressing Shape Mismatch Error in TensorFlow Code for (None, 224, 224, 3) vs. (TensorSpec(shape=(None, None, 224, 224, 3)) Shape: Troubleshooting and Resolution

General Discussion
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#1

**code **

from google.colab import drive

drive.mount(‘/content/gdrive’)
Mounted at /content/gdrive

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

import tensorflow_datasets as tfds

from tensorflow.keras.layers import Conv2D , MaxPool2D,Dense,Flatten,InputLayer,BatchNormalization

from tensorflow.keras.losses import BinaryCrossentropy

from tensorflow.keras.optimizers import Adam

dataset_path = ‘/content/gdrive/MyDrive/Dataset malaria/Train’

dataset_info = tfds.builder(“malaria”)

dataset_info = dataset_info.info

dataset = tf.keras.preprocessing.image_dataset_from_directory(
dataset_path,
validation_split=0.2,
subset=“training”,
seed=1337,
image_size=(124, 121),
batch_size=32,

)

Found 416 files belonging to 2 classes.
Using 333 files for training.

train_dataset = dataset
val_dataset = tf.keras.preprocessing.image_dataset_from_directory(
dataset_path,
validation_split=0.2,
subset=“validation”,
seed=1337,
image_size=(224, 224),
batch_size=32,
)

Found 416 files belonging to 2 classes.
Using 83 files for validation.

test_dataset = tf.keras.preprocessing.image_dataset_from_directory(
dataset_path,
validation_split=0.1, # Adjust as needed
subset=“validation”,
seed=1337,
image_size=(124, 121),
batch_size=32,
)

Found 416 files belonging to 2 classes.
Using 41 files for validation.

def splits(dataset, TRAIN_RATIO, VAL_RATIO, TEST_RATIO):
DATASET_SIZE = len(dataset)

train_dataset = dataset.take(int(TRAIN_RATIO*DATASET_SIZE))

val_test_dataset = dataset.skip(int(TRAIN_RATIODATASET_SIZE))
val_dataset = val_test_dataset.take(int(VAL_RATIODATASET_SIZE))

test_dataset = val_test_dataset.skip(int(VAL_RATIO*DATASET_SIZE))
return train_dataset, val_dataset, test_dataset

TRAIN_RATIO = 0.8

VAL_RATIO = 0.1

TEST_RATIO = 0.1

train_dataset, val_dataset, test_dataset = splits(train_dataset, TRAIN_RATIO, VAL_RATIO, TEST_RATIO)

print(list(train_dataset.take(1).as_numpy_iterator()), list(val_dataset.take(1).as_numpy_iterator()), list(test_dataset.take(1).as_numpy_iterator()))

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     ...,
     [0., 0., 0.],
     [0., 0., 0.],
     [0., 0., 0.]]],


   [[[0., 0., 0.],
     [0., 0., 0.],
     [0., 0., 0.],
     ...,
     [0., 0., 0.],
     [0., 0., 0.],
     [0., 0., 0.]],

    [[0., 0., 0.],
     [0., 0., 0.],
     [0., 0., 0.],
     ...,
     [0., 0., 0.],
     [0., 0., 0.],
     [0., 0., 0.]],

    [[0., 0., 0.],
     [0., 0., 0.],
     [0., 0., 0.],
     ...,
     [0., 0., 0.],
     [0., 0., 0.],
     [0., 0., 0.]],

    ...,

    [[0., 0., 0.],
     [0., 0., 0.],
     [0., 0., 0.],
     ...,
     [0., 0., 0.],
     [0., 0., 0.],
     [0., 0., 0.]],

    [[0., 0., 0.],
     [0., 0., 0.],
     [0., 0., 0.],
     ...,
     [0., 0., 0.],
     [0., 0., 0.],
     [0., 0., 0.]],

    [[0., 0., 0.],
     [0., 0., 0.],
     [0., 0., 0.],
     ...,
     [0., 0., 0.],
     [0., 0., 0.],
     [0., 0., 0.]]]], dtype=float32), array([1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,
   1, 1, 1, 0, 1, 1, 0, 1, 1, 0], dtype=int32))]

[ ] **

for i, (images, labels) in enumerate(train_dataset.take(1)): # Taking 1 batch for simplicity
for j in range(16): # Displaying 16 images
ax = plt.subplot(4, 4, j + 1)
plt.imshow(images[j].numpy().astype(“uint8”)) # Convert to NumPy array and adjust data type
plt.title(dataset_info.features[‘label’].int2str(labels[j].numpy())) # Convert label to NumPy array
plt.axis(‘off’)

plt.show()

dataset_info.features[‘label’].int2str(1)
uninfected

for data in dataset.take(4):
print(data)

IM_SIZE = 224
def resize_rescale(image,label):
return tf.image.resize(image,(IM_SIZE,IM_SIZE))/255.0,label

train_dataset = train_dataset.map(resize_rescale)

val_dataset = val_dataset.map(resize_rescale)

test_dataset = test_dataset.map(resize_rescale)

train_dataset

** MapDataset element_spec=(TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int32, name=None))**

BATCH_SIZE = 32

train_dataset= train_dataset.shuffle(buffer_size = 8, reshuffle_each_iteration = True).batch(32).prefetch(tf.data.AUTOTUNE)

BATCH_SIZE = 32

val_dataset= val_dataset.shuffle(buffer_size = 8, reshuffle_each_iteration = True).batch(32).prefetch(tf.data.AUTOTUNE)

train_dataset
<_PrefetchDataset element_spec=(TensorSpec(shape=(None, None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, None), dtype=tf.int32, name=None))>

** ERROR OCCUR here is mismatch shape input need 4 tensorshape but code provide 5 tensor shape (code run fine but error occur on last epoche line) **

val_dataset
<_PrefetchDataset element_spec=(TensorSpec(shape=(None, None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, None), dtype=tf.int32, name=None))>

IM_SIZE = 224
lenet_model = tf.keras.Sequential([
InputLayer(input_shape = (IM_SIZE,IM_SIZE, 3)),

Conv2D(filters = 6 , kernel_size = 5, strides=1, padding = 'valid', activation = 'relu' ),
BatchNormalization(),
MaxPool2D (pool_size = 2, strides=2),

Conv2D(filters = 16 , kernel_size = 3, strides=1, padding = 'valid', activation = 'relu' ),
BatchNormalization(),
MaxPool2D (pool_size = 2, strides=2),

Flatten(),
Dense(100,activation = 'relu'),
BatchNormalization(),
Dense(10,activation = 'relu'),
BatchNormalization(),
Dense(1,activation = 'sigmoid'),

])
lenet_model.summary()

**Model: “sequential_6”

Layer (type) Output Shape Param #

conv2d_12 (Conv2D) (None, 220, 220, 6) 456

batch_normalization_24 (Ba (None, 220, 220, 6) 24
tchNormalization)

max_pooling2d_12 (MaxPooli (None, 110, 110, 6) 0
ng2D)

conv2d_13 (Conv2D) (None, 108, 108, 16) 880

batch_normalization_25 (Ba (None, 108, 108, 16) 64
tchNormalization)

max_pooling2d_13 (MaxPooli (None, 54, 54, 16) 0
ng2D)

flatten_6 (Flatten) (None, 46656) 0

dense_18 (Dense) (None, 100) 4665700

batch_normalization_26 (Ba (None, 100) 400
tchNormalization)

dense_19 (Dense) (None, 10) 1010

batch_normalization_27 (Ba (None, 10) 40
tchNormalization)

dense_20 (Dense) (None, 1) 11

=================================================================
Total params: 4668585 (17.81 MB)
Trainable params: 4668321 (17.81 MB)
Non-trainable params: 264 (1.03 KB)**

y_true = [0,1,0,0]

y_pred = [0.6,0.51,0.94,1]

bce = tf.keras.losses.BinaryCrossentropy()

bce(y_true, y_pred)

<tf.Tensor: shape=(), dtype=float32, numpy=4.9340706>

lenet_model.compile(optimizer = Adam(learning_rate = 0.01),
loss = BinaryCrossentropy(),
metrics = ‘accuracy’)

ONLY ERROR IN LAST LINE BECAUSE OF SHAPE MISMATCH

history = lenet_model.fit(train_dataset,validation_data = val_dataset, epochs = 20, verbose=1)

** ValueError: Input 0 of layer “sequential_6” is incompatible with the layer: expected shape=(None, 224, 224, 3), found shape=(None, None, 224, 224, 3)**

#2

provide me solution please

#3

Hi @Palak_Talreja
The formatting of your post/question is awful, and makes it difficult to help (at least for me!)
Maybe you can share the Google Colab?
Thank you.

1 Like
#5

can you send me your mail I will send you

#7

@tagoma provide your E-mail

#8

@tagoma here is the link to open my google colab file please solve the problem

#10

Hi @Palak_Talreja, I have gone through your code and i have a few suggestions, while loading the dataset you can pass the desired shape (224,224) so that you don’t need to use the resize_rescale function. For rescaling you can pass the rescaling layer in the model

lenet_model = tf.keras.Sequential([
    InputLayer(input_shape = (224,224, 3)),
    Rescaling(1./127.5,offset=-1),
    Conv2D(filters = 6 , kernel_size = 5, strides=1, padding = 'valid', activation = 'relu' ),
    BatchNormalization(),
    MaxPool2D (pool_size = 2, strides=2),

    Conv2D(filters = 16 , kernel_size = 3, strides=1, padding = 'valid', activation = 'relu' ),
    BatchNormalization(),
    MaxPool2D (pool_size = 2, strides=2),

    Flatten(),
    Dense(100,activation = 'relu'),
    BatchNormalization(),
    Dense(10,activation = 'relu'),
    BatchNormalization(),
    Dense(1,activation = 'sigmoid'),


])
lenet_model.summary()

And also in the colab i can see the Image shape before resizing: (8, 32, 224, 224, 3) but there is no code mentioned for this output.

while training the model, the model expects the training data to be in (Batch_size,height,width,channels). But you are passing the dataset of shape (8, 32, 224, 224, 3) to the model so you are getting the error.

I have trained the model with the above mentioned changes and did not face any error. Please refer to this gist for working code. Thank You.

#12

its great code is working now thankyou so much dear for helping us :smiling_face_with_three_hearts: :heart: :heart: :heart: