2 replies

How do I fix a tensor dimension mismatch in TinyML disease detection?

Hello guys am workin on Disease Detection from X-Ray Scans Using TinyML, i have gathered a diverse dataset of X-ray images from public medical databases, used images labeled with specific diseases or conditions, such as pneumonia, tuberculosis, or normal/healthy cases, i have also prepared my training script but keep getting an error while training the model

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "tensorflow/lite/python/interpreter.py", line 42, in set_tensor
    self._interpreter.SetTensor(self._tensor_index_map[tensor_index], value)
ValueError: Cannot set tensor: Dimension mismatch. Got [1, 128, 128, 3], expected [1, 64, 64, 1]

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "tensorflow/lite/python/interpreter.py", line 42, in set_tensor
    self._interpreter.SetTensor(self._tensor_index_map[tensor_index], value)
ValueError: Cannot set tensor: Dimension mismatch. Got [1, 128, 128, 3], expected [1, 64, 64, 1]

here's my code

import tensorflow as tf
from tensorflow.keras import layers, models

model = models.Sequential([
    layers.Conv2D(16, (3, 3), activation='relu', input_shape=(64, 64, 1)),
    layers.MaxPooling2D((2, 2), name="pool_1"),
    layers.Conv2D(32, (3, 3), activation='relu', name="conv_2"),
    layers.MaxPooling2D((2, 2), name="pool_2"),
    layers.Flatten(name="flatten"),
    layers.Dense(64, activation='relu', name="dense_1"),
    layers.Dense(3, activation='softmax', name="output_layer")
])

import tensorflow as tf
from tensorflow.keras import layers, models

model = models.Sequential([
    layers.Conv2D(16, (3, 3), activation='relu', input_shape=(64, 64, 1)),
    layers.MaxPooling2D((2, 2), name="pool_1"),
    layers.Conv2D(32, (3, 3), activation='relu', name="conv_2"),
    layers.MaxPooling2D((2, 2), name="pool_2"),
    layers.Flatten(name="flatten"),
    layers.Dense(64, activation='relu', name="dense_1"),
    layers.Dense(3, activation='softmax', name="output_layer")
])

How do I fix a tensor dimension mismatch in TinyML disease detection?

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "tensorflow/lite/python/interpreter.py", line 42, in set_tensor
    self._interpreter.SetTensor(self._tensor_index_map[tensor_index], value)
ValueError: Cannot set tensor: Dimension mismatch. Got [1, 128, 128, 3], expected [1, 64, 64, 1]

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "tensorflow/lite/python/interpreter.py", line 42, in set_tensor
    self._interpreter.SetTensor(self._tensor_index_map[tensor_index], value)
ValueError: Cannot set tensor: Dimension mismatch. Got [1, 128, 128, 3], expected [1, 64, 64, 1]

here's my code

import tensorflow as tf
from tensorflow.keras import layers, models

model = models.Sequential([
    layers.Conv2D(16, (3, 3), activation='relu', input_shape=(64, 64, 1)),
    layers.MaxPooling2D((2, 2), name="pool_1"),
    layers.Conv2D(32, (3, 3), activation='relu', name="conv_2"),
    layers.MaxPooling2D((2, 2), name="pool_2"),
    layers.Flatten(name="flatten"),
    layers.Dense(64, activation='relu', name="dense_1"),
    layers.Dense(3, activation='softmax', name="output_layer")
])

import tensorflow as tf
from tensorflow.keras import layers, models

model = models.Sequential([
    layers.Conv2D(16, (3, 3), activation='relu', input_shape=(64, 64, 1)),
    layers.MaxPooling2D((2, 2), name="pool_1"),
    layers.Conv2D(32, (3, 3), activation='relu', name="conv_2"),
    layers.MaxPooling2D((2, 2), name="pool_2"),
    layers.Flatten(name="flatten"),
    layers.Dense(64, activation='relu', name="dense_1"),
    layers.Dense(3, activation='softmax', name="output_layer")
])

How do I fix a tensor dimension mismatch in TinyML disease detection?

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