[英]TensorFlow Recommenders - ValueError: Shape must be rank 2 but is rank 3
[英]Tensorflow: ValueError: Shape must be rank 4 but is rank 5
我是機器學習和 tensorflow 的新手。 我正在 keras 中使用功能 API 創建網絡並出現錯誤。
ValueError: Shape must be rank 4 but is rank 5 for '{{node max_pooling2d_13/MaxPool}} = MaxPool[T=DT_FLOAT, data_format="NHWC", ksize=[1, 8, 8, 1], padding="SAME", strides=[1, 8, 8, 1]](max_pooling2d_13/MaxPool/input)' with input shapes: [1,?,64,64,8].
感謝您的幫助,我在這里有點迷茫。
我的輸入是:
(64,64,3)
這是我的功能:
def convolutional_model(input_shape):
"""
Implements the forward propagation for the model:
CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> DENSE
"""
input_img = tf.keras.Input(shape=input_shape)
print(input_img)
## CONV2D: 8 filters 4x4, stride of 1, padding 'SAME'
# Z1 = None
## RELU
# A1 = None
## MAXPOOL: window 8x8, stride 8, padding 'SAME'
# P1 = None
## CONV2D: 16 filters 2x2, stride 1, padding 'SAME'
# Z2 = None
## RELU
# A2 = None
## MAXPOOL: window 4x4, stride 4, padding 'SAME'
# P2 = None
## FLATTEN
# F = None
## Dense layer
## 6 neurons in output layer.
# outputs = None
Z1 = tfl.Conv2D(8, 4 ,strides = (1, 1) , padding='same')(input_img),
A1 = tfl.ReLU()(Z1),
P1 = tfl.MaxPool2D(pool_size=(8, 8), strides=(8, 8), padding='same')(A1),
Z2 = tfl.Conv2D(16, (2, 2), strides = (1, 1), padding ="same")(P1),
A2 = tfl.ReLU()(Z2),
P2 = tfl.MaxPool2D(pool_size = (4, 4), strides=(4, 4) , padding='same')(A2),
F = tfl.Flatten()(P2),
outputs = tfl.Dense(units= 6 , activation='softmax')(F),
model = tf.keras.Model(inputs=input_img, outputs=outputs)
return model
看
輸入形狀:[1,?,64,64,8]
我認為不知何故,您在輸入張量中添加了一個額外的維度。 我不確定為什么會發生這種情況,因為我從來沒有遇到過這種情況。
我嘗試將您的模型重寫為 tf.keras.Sequential 模型,它似乎有效
result = tf.keras.Sequential()
result.add(tf.keras.layers.Conv2D(8, 4 ,strides = 1 , padding='same'))
result.add(tf.keras.layers.ReLU())
result.add(tf.keras.layers.MaxPool2D(pool_size=(8, 8), strides=8, padding='same'))
result.add(tf.keras.layers.Conv2D(16, 4 ,strides = 1 , padding='same'))
result.add(tf.keras.layers.ReLU())
result.add(tf.keras.layers.MaxPool2D(pool_size=(4, 4), strides=4, padding='same'))
result.add(tf.keras.layers.Flatten())
x=result(input_img)
outputs = tf.keras.layers.Dense(units= 6 , activation='softmax')(x),
model = tf.keras.Model(inputs=input_img, outputs=outputs)
調用model.summary()后,我回來了:
input_1 (InputLayer)-------------[(None, 64, 64, 3)]--------------0
順序(Sequential)------------(無,64)-----------2456
密集(Dense)----------------------(無,6)------------------- --390
這是您要找的形狀嗎?
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