| ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape-CodePudding

import numpy as np
import matplotlib.pyplot as plt
import cv2 as cv
import tensorflow as tf
from tensorflow.keras import datasets, layers, models

fashion_mnist = tf.keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

train_images, test_images = train_images / 255, test_images / 255

model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10)
])

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

model.fit(train_images, train_labels, epochs=10)

test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

print('\nTest accuracy:', test_acc)

probability_model = tf.keras.Sequential([model,
                                         tf.keras.layers.Softmax()])

predictions = probability_model.predict(test_images)

predictions[0]

model.save("image_classifier.model")
model = models.load_model("image_classifier.model")

img = cv.imread("shoes.png")
img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
plt.imshow(img, cmap = plt.cm.binary)

prediction = model.predict(np.array(img) / 255)
index = np.argmax(prediction)
print(f"Prediction is {class_names[index]}")

when I tried to run this code with new image I get error all the time. I want the model to make a new prediction with new image but always I get scale error. my error is:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
----> 1 prediction = model.predict(np.array(img) / 255)
      2 index = np.argmax(prediction)
      3 print(class_names[index])

~/opt/anaconda3/lib/python3.9/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
     68     finally:
     69       del filtered_tb

~/opt/anaconda3/lib/python3.9/site-packages/keras/engine/training.py in tf__predict_function(iterator)
     13                 try:
     14                     do_return = True
---> 15                     retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
     16                 except:
     17                     do_return = False

ValueError: in user code:

    File "/Users/.../opt/anaconda3/lib/python3.9/site-packages/keras/engine/training.py", line 1845, in predict_function  *
        return step_function(self, iterator)
    File "/Users/.../opt/anaconda3/lib/python3.9/site-packages/keras/engine/training.py", line 1834, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/Users/.../opt/anaconda3/lib/python3.9/site-packages/keras/engine/training.py", line 1823, in run_step  **
        outputs = model.predict_step(data)
    File "/Users/.../opt/anaconda3/lib/python3.9/site-packages/keras/engine/training.py", line 1791, in predict_step
        return self(x, training=False)
    File "/Users/.../opt/anaconda3/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
        raise e.with_traceback(filtered_tb) from None
    File "/Users/.../opt/anaconda3/lib/python3.9/site-packages/keras/engine/input_spec.py", line 264, in assert_input_compatibility
        raise ValueError(f'Input {input_index} of layer "{layer_name}" is '

` ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 28, 28), found shape=(None, 28, 3)

CodePudding user response：

It's only failing on the new image you passed in. Here:

img = cv.imread("shoes.png")
img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
plt.imshow(img, cmap = plt.cm.binary)

prediction = model.predict(np.array(img) / 255)

Loading that png image returning an image with three channels: shape [height, width, channels]. Your model is expecting inputs with shape [batch, height, width].

You need to convert the image to greyscale and add a batch dimension:

img = np.array(img)/255
img = np.mean(img, axis=-1]
img = img[np.newaxis, ...]

prediction = model.predict(img)