[英]Keras Conv2D - ValueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=4, found ndim=3
I konw, there are already some questions like this, but I couldn´t find any solution for this problem.我知道,已经有一些这样的问题,但我找不到这个问题的任何解决方案。
I created a model like this:我创建了一个这样的模型:
def CreateModel(optimizer=optimizer, loss=loss, learn_rate=learn_rate, activity_regularizer=activity_regularizer):
model = keras.Sequential([
keras.layers.Conv2D(32,3,input_shape=(9,21,1)),
keras.layers.Flatten(),
keras.layers.Dense(32, activation='relu', kernel_initializer=keras.initializers.RandomUniform(maxval=1, minval=0), bias_initializer=keras.initializers.Zeros(), activity_regularizer=activity_regularizer),
keras.layers.Dense(2, activation='softmax', kernel_initializer=keras.initializers.RandomUniform(maxval=1, minval=0), bias_initializer=keras.initializers.Zeros(), activity_regularizer=activity_regularizer)
])
model.compile(optimizer=optimizer,
loss=loss,
metrics=['accuracy', keras.metrics.FalseNegatives(), keras.metrics.FalsePositives(), keras.metrics.Precision(), keras.metrics.Recall()])
return model
My input consists of 300 9x21 gray scale images.我的输入由 300 张 9x21 灰度图像组成。
Without the Conv2D layer it works perfectly fine.没有 Conv2D 层,它工作得很好。 But with this Conv2D layer I got the error:
但是使用这个 Conv2D 层我得到了错误:
ValueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=4, found ndim=3
I also tried some other input_shapes like:我还尝试了其他一些 input_shapes,例如:
keras.layers.Conv2D(32,3,input_shape=(300,9,21,1))
keras.layers.Conv2D(32,3,input_shape=(300,9,21))
but without success.但没有成功。
Thanks Prickels谢谢皮克尔斯
Prickels,皮克尔斯,
Just make sure that you feed the model with data in [n_items,9,21,1] shape.只需确保为模型提供 [n_items,9,21,1] 形状的数据。 use
data = tf.expand_dims(data, axis =-1)
使用
data = tf.expand_dims(data, axis =-1)
Alternatively add Reshape layer first:或者先添加 Reshape 图层:
tf.keras.layers.Reshape((9,21,1), input_shape=(9,21))
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