层序的输入 0 与层不兼容：：预期 min_ndim=4，发现 ndim=2。 收到的完整形状：（无，1）

Question

I am trying to get the prediction of my model

prediction = model.predict(validation_names)
print(prediction)

but I get the following error:

ValueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=4, found ndim=2. Full shape received: (None, 1)

I understand that this is due to the fact that the model accepts data of dimension 4

Model:

model = tf.keras.models.Sequential([

          tf.keras.layers.Conv2D(16, (3,3), activation = 'relu', 
                                        input_shape = (300, 300, 3)),
          tf.keras.layers.MaxPooling2D(2,2),

          tf.keras.layers.Conv2D(32, (3,3), activation = 'relu'),
          tf.keras.layers.MaxPooling2D(2,2),

          tf.keras.layers.Conv2D(64, (3,3), activation = 'relu'),
          tf.keras.layers.MaxPooling2D(2,2),

          tf.keras.layers.Conv2D(64, (3,3), activation = 'relu'),
          tf.keras.layers.MaxPooling2D(2,2),

          tf.keras.layers.Flatten(),
          tf.keras.layers.Dense(512, activation = 'relu'),

          tf.keras.layers.Dense(3, activation = 'softmax')
])

How can I process the prediction data to solve this problem?

Answer 1

Conv2D expects 4+D tensor with shape: batch_shape + (channels, rows, cols) if data_format='channels_first' or 4+D tensor with shape: batch_shape + (rows, cols, channels) if data_format='channels_last'

Working sample code:

# The inputs are 28x28 RGB images with `channels_last` and the batch
# size is 4.
import tensorflow as tf
input_shape = (4, 28, 28, 3)
x = tf.random.normal(input_shape)
y = tf.keras.layers.Conv2D(2, 3, activation='relu', input_shape=input_shape[1:])(x)