[英]keras for adding two dense layers
有两个输入 x 和 u,它们生成输出 y。 x、u和y之间存在线性关系,即y = x wx + u wx。 我正在尝试根据数据计算 wx 和 wu。 这是模型构建/拟合的代码。
n_train = 400
n_val = 100
train_u = u[:(n_train+n_val)]
train_x = x[:(n_train+n_val)]
train_y = y[:(n_train+n_val)]
test_u = u[(n_train+n_val):]
test_x = x[(n_train+n_val):]
test_y = y[(n_train+n_val):]
val_u = train_u[-n_val:]
val_x = train_x[-n_val:]
val_y = train_y[-n_val:]
train_u = train_u[:-n_val]
train_x = train_x[:-n_val]
train_y = train_y[:-n_val]
# RNN derived classes want a shape of (batch_size, timesteps, input_dim)
# batch_size. One sequence is one sample. A batch is comprised of one or more samples.
# timesteps. One time step is one point of observation in the sample.
# input_dim. number of observation at a time step.
# I believe n_train = one_epoch = batch_size * time_steps, features = nx_lags or nu_lags
# I also thing an epoch is one pass through the training data
n_batches_per_epoch = 8
n_iterations_per_batch = round(n_train / n_batches_per_epoch)
batch_size = n_batches_per_epoch
time_steps = n_iterations_per_batch
features_x = train_x.shape[1]
features_u = train_u.shape[1]
features_y = train_y.shape[1]
keras_train_u = train_u.values.reshape((batch_size, time_steps, features_u))
keras_train_x = train_x.values.reshape((batch_size, time_steps, features_x))
keras_train_y = train_y.reshape((batch_size, time_steps, features_y))
keras_val_u = val_u.values.reshape((2, time_steps, features_u))
keras_val_x = val_x.values.reshape((2, time_steps, features_x))
keras_val_y = val_y.reshape((2, time_steps, features_y))
keras_test_u = test_u.values.reshape((1, test_u.shape[0], features_u))
keras_test_x = test_x.values.reshape((1, test_u.shape[0], features_x))
keras_test_y = test_y.reshape((1, test_u.shape[0], features_y))
print('u.values.shape: ', u.values.shape)
# Now try a tensorflow model
# x_input = keras.Input(shape=(batch_size, time_steps, features_x), name='x_input')
# u_input = keras.Input(shape=(batch_size, time_steps, features_u), name='u_input')
x_input = keras.Input(shape=(time_steps, features_x), name='x_input')
u_input = keras.Input(shape=(time_steps, features_u), name='u_input')
da = layers.Dense(ny, name='dense_a', use_bias=False)(x_input)
db = layers.Dense(ny, name='dense_b', use_bias=False)(u_input)
output = layers.Add()([da, db])
model = keras.Model(inputs=[x_input, u_input], outputs=output)
model.compile(optimizer=keras.optimizers.RMSprop(), # Optimizer
# Loss function to minimize
loss=keras.losses.SparseCategoricalCrossentropy(),
# List of metrics to monitor
metrics=[keras.metrics.SparseCategoricalAccuracy()])
print(model.summary())
print('keras_train_x.shape: ', keras_train_x.shape)
print('keras_train_u.shape: ', keras_train_u.shape)
print('keras_train_y.shape: ', keras_train_y.shape)
print('keras_val_x.shape: ', keras_val_x.shape)
print('keras_val_u.shape: ', keras_val_u.shape)
print('keras_val_y.shape: ', keras_val_y.shape)
history = model.fit([keras_train_x, keras_train_u], keras_train_y,
batch_size=64,
epochs=3,
# We pass some validation for
# monitoring validation loss and metrics
# at the end of each epoch
validation_data=([keras_val_x, keras_val_u], keras_val_y))
而且,这是输出,有错误。
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
x_input (InputLayer) [(None, 50, 7)] 0
__________________________________________________________________________________________________
u_input (InputLayer) [(None, 50, 7)] 0
__________________________________________________________________________________________________
dense_a (Dense) (None, 50, 2) 14 x_input[0][0]
__________________________________________________________________________________________________
dense_b (Dense) (None, 50, 2) 14 u_input[0][0]
__________________________________________________________________________________________________
add (Add) (None, 50, 2) 0 dense_a[0][0]
dense_b[0][0]
==================================================================================================
Total params: 28
Trainable params: 28
Non-trainable params: 0
__________________________________________________________________________________________________
None
keras_train_x.shape: (8, 50, 7)
keras_train_u.shape: (8, 50, 7)
keras_train_y.shape: (8, 50, 2)
keras_val_x.shape: (2, 50, 7)
keras_val_u.shape: (2, 50, 7)
keras_val_y.shape: (2, 50, 2)
Train on 8 samples, validate on 2 samples
Epoch 1/3
Traceback (most recent call last):
File "arx_rnn.py", line 487, in <module>
main()
File "/arx_rnn.py", line 481, in main
rnn_prediction = x.rnn_n_steps(y_measured, u_control, n_to_predict)
File "arx_rnn.py", line 387, in rnn_n_steps
validation_data=([keras_val_x, keras_val_u], keras_val_y))
File "venv\lib\site-packages\tensorflow\python\keras\engine\training.py", line 780, in fit
steps_name='steps_per_epoch')
File "venv\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py", line 363, in model_iteration
batch_outs = f(ins_batch)
File "venv\lib\site-packages\tensorflow\python\keras\backend.py", line 3292, in __call__
run_metadata=self.run_metadata)
File "venv\lib\site-packages\tensorflow\python\client\session.py", line 1458, in __call__
run_metadata_ptr)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Can not squeeze dim[2], expected a dimension of 1, got 2
[[{{node metrics/sparse_categorical_accuracy/Squeeze}}]]
Process finished with exit code 1
错误消息告诉我什么,以及如何纠正?
Keras 分类准确度指标期望输出、标签、形状为(batch_size,num_classes)
。 错误消息中的dim[2]
表示输出形状为 3d: (None,50,2)
简单的解决方法是,以确保以任何手段,使输出层给每个类每批一个预测-即,具有形状(batch_size,num_classes)
-其可以通过进行Reshape
,或Flatten
。
更好的解决方法是根据设计需求改变您的输入-输出拓扑 - 即,您到底在分类什么? 您的数据维度表明您寻求对各个时间步长进行分类 - 在这种情况下,一次一个时间步长提供数据: (batch_size,features)
。 或者,在批处理轴中输入时间步长,一次一个批处理,因此 1000 个时间步长将对应于(1000,features)
- 但如果模型具有任何stateful
层,则不要这样做,它将每个批处理轴条目视为一个独立的序列。
再次使用timesteps>1
对序列进行分类,确保层数据流最终产生 2d 输出。
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