Question

我在My Cloud Firestore中有一些不同的收藏，

ProjectList1
ProjectList2
++

我想用菜单项切换它们，但在'firestore.collection'中我只坐 ProjectList1 ，现在我该如何设置或切换 ProjectList2或ProjectList3 在firestore.collection（ProjectList1）..

代码如下：

flex

我尝试切换菜单，但没有正常工作，它先保留所有以前的数据..

from keras.layers import Input, Embedding, LSTM, Dense
from keras.models import Model

headline_data=[[i for i in range(100)]]
additional_data=[[100,200]]
labels=[1,2]
# Headline input: meant to receive sequences of 100 integers, between 1 and 10000.

# Note that we can name any layer by passing it a "name" argument.
main_input = Input(shape=(100,), dtype='int32', name='main_input')

# This embedding layer will encode the input sequence
# into a sequence of dense 512-dimensional vectors.
x = Embedding(output_dim=512, input_dim=10000, input_length=100)(main_input)

# A LSTM will transform the vector sequence into a single vector,
# containing information about the entire sequence
lstm_out = LSTM(32)(x)


auxiliary_output = Dense(1, activation='sigmoid', name='aux_output')(lstm_out)

auxiliary_input = Input(shape=(5,), name='aux_input')
x = keras.layers.concatenate([lstm_out, auxiliary_input])

# We stack a deep densely-connected network on top
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)

# And finally we add the main logistic regression layer
main_output = Dense(1, activation='sigmoid', name='main_output')(x)


# This defines a model with two inputs and two outputs:
model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output, auxiliary_output])

# We compile the model and assign a weight of 0.2 to the auxiliary loss. 
# To specify different  loss_weights or loss for each different output, 
# you can use a list or a dictionary. Here we pass a single loss as the loss argument, 
# so the same loss will be used on all outputs.

# Since our inputs and outputs are named (we passed them a "name" argument), We could also have compiled the model via:
model.compile(optimizer='rmsprop',
              loss={'main_output': 'binary_crossentropy', 'aux_output': 'binary_crossentropy'},
              loss_weights={'main_output': 1., 'aux_output': 0.2})

# And trained it via:
model.fit({'main_input': headline_data, 'aux_input': additional_data},
          {'main_output': labels, 'aux_output': labels},
          epochs=50, batch_size=32)

Answer 1

在从Firebase添加更多内容之前，您需要清除现有列表中的数据

{{1}}

如何使用菜单充气器更改Firestore Collection？

1 个答案: