Shape must be rank 1 but is rank 2
Webb15 sep. 2024 · Solution 1 The shape of constant x is (2,), i.e. a one-dimensional array, and you are trying to multiply it with a two-dimensional array w1 of shape (2, 3), which is not … Webbbreakfast 286 views, 8 likes, 3 loves, 4 comments, 0 shares, Facebook Watch Videos from Inspiration FM 92.3: PAPER VIEW WITH AZU OSUMILI ON BREAKFAST JAM
Shape must be rank 1 but is rank 2
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Webb18 feb. 2024 · You can use tf.expand_dims(a,0) and tf.expand_dims(b,1) to have rank 2 shapes. Try the following code: a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], name='a') b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], name='b') c = tf.matmul(tf.expand_dims(a,0), … Webb31 jan. 2024 · 1. tensorflow中conv2d卷积shape错误 错误信息： ValueError: Shape must be rank 4 but is rank 0 for ‘Conv2D’ (op: ‘Conv2D’) with input shape:[32, 299, 299, 3], [] 错误原 …
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Webb20 sep. 2024 · tf.concat () axis = 0 means concatenating in 1st dim. axis = 1 means concatenating in 2nd dim. axis = n-1 means concatenating in n th dim. ValueError: Shape …
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