大家好,我尝试用AlexNet + LSTM建立模型,使用原始图像作为输入。
但我遇到了这样一个错误:
ValueError: Input 0 of layer lstm_5 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 43264)
我的模型代码:
model = tf.keras.models.Sequential([
# 1st conv
tf.keras.layers.Conv2D(96, (11,11),strides=(4,4), ac
def build_model(layers):
model = Sequential()
# By setting return_sequences to True we are able to stack another LSTM layer
model.add(LSTM(layers[0], input_shape=(1, 2), return_sequences=True))
model.add(LSTM(layers[0], input_shape=(1, 2),
return_sequences=False))
model
我收到了来自Keras的非常混乱的错误消息。我使用以下模型,并将其传递给形状(num_examples, n, 1)的输入。
def create_model():
model = Sequential()
model.add(LSTM(64, input_shape=(n,1), return_sequences=False))
model.add(Dense(units=n, activation='linear'))
return model
我收到一条错误消息:ValueError: Error when checking target:
我有CNN并希望将其更改为LSTM,但当我修改代码时,收到相同的错误: ValueError:输入0与图层不兼容gru_1:期望的ndim=3,找到ndim=4
我已经更改了ndim,但没有起作用。
关注我的cnn
def build_model(X,Y,nb_classes):
nb_filters = 32 # number of convolutional filters to use
pool_size = (2, 2) # size of pooling area for max pooling
kernel_size = (3, 3) # convol
我正在尝试构建一个LSTM模型,在中处理文档示例。
from keras.models import Sequential
from keras.layers import LSTM
以下三行代码(加上注释)直接取自上面的文档链接:
model = Sequential()
model.add(LSTM(32, input_dim=64, input_length=10))
# for subsequent layers, not need to specify the input size:
model.add(LSTM(16))
ValueError:输入0与lstm_2层不兼容:预期
我正在看一本关于Python的深度学习的书,来自F.Chollet。
我试着按照代码示例来做。我刚刚安装了keras,在尝试运行以下代码时,我收到了这个错误:从这个笔记本:
from keras import models
from keras import layers
network = models.Sequential()
network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,)))
network.add(layers.Dense(10, activation='softm