有没有办法让一个简单的神经网络适应张量输入数据及其必须只有一个数字的输出？
Is there a way to fit a simple neural network to an input data which is a tensor and its ouputs which must be just one number?
我有这段代码，我试图在其中拟合一个只有三层的神经网络模型：输入层、隐藏层以及最后必须只有一个神经元的输出层对于单个输出。问题是，在进行拟合时，我总是获得相同的准确度值（空值）和损失值（保持不变），并且我尝试将优化器从 'sgd' 更改为 'adam'仍然一切正常。你会推荐什么？
Layer (type) Output Shape Param N°
=================================================================
data_in (InputLayer) [(None, 4, 256)] 0
dense (Dense) (None, 4, 124) 31868
dense_1 (Dense) (None, 4, 1) 125
=================================================================
Total params: 31,993
Trainable params: 31,993
Non-trainable params: 0
_________________________________________________________________
Epoch 1/20
20/20 [==============================] - 8s 350ms/step - loss: 0.3170 - accuracy: 1.7361e-05
Epoch 2/20
20/20 [==============================] - 7s 348ms/step - loss: 0.2009 - accuracy: 6.7817e-08
Epoch 3/20
20/20 [==============================] - 7s 348ms/step - loss: 0.0513 - accuracy: 0.0000e+00
Epoch 4/20
20/20 [==============================] - 7s 348ms/step - loss: 0.0437 - accuracy: 0.0000e+00
Epoch 5/20
20/20 [==============================] - 7s 346ms/step - loss: 0.0430 - accuracy: 0.0000e+00
Epoch 6/20
20/20 [==============================] - 7s 346ms/step - loss: 0.0428 - accuracy: 0.0000e+00
Epoch 7/20
20/20 [==============================] - 7s 345ms/step - loss: 0.0428 - accuracy: 0.0000e+00
Epoch 8/20
20/20 [==============================] - 7s 345ms/step - loss: 0.0430 - accuracy: 0.0000e+00
Epoch 9/20
20/20 [==============================] - 7s 348ms/step - loss: 0.0429 - accuracy: 0.0000e+00
Epoch 10/20
20/20 [==============================] - 7s 348ms/step - loss: 0.0429 - accuracy: 0.0000e+00
Epoch 11/20
20/20 [==============================] - 7s 346ms/step - loss: 0.0429 - accuracy: 0.0000e+00
Epoch 12/20
20/20 [==============================] - 7s 344ms/step - loss: 0.0428 - accuracy: 0.0000e+00
Epoch 13/20
20/20 [==============================] - 7s 348ms/step - loss: 0.0428 - accuracy: 0.0000e+00
Epoch 14/20
20/20 [==============================] - 7s 345ms/step - loss: 0.0433 - accuracy: 0.0000e+00
Epoch 15/20
20/20 [==============================] - 7s 345ms/step - loss: 0.0430 - accuracy: 0.0000e+00
Epoch 16/20
20/20 [==============================] - 7s 347ms/step - loss: 0.0432 - accuracy: 0.0000e+00
Epoch 17/20
20/20 [==============================] - 7s 346ms/step - loss: 0.0429 - accuracy: 0.0000e+00
Epoch 18/20
20/20 [==============================] - 7s 347ms/step - loss: 0.0430 - accuracy: 0.0000e+00
Epoch 19/20
20/20 [==============================] - 7s 348ms/step - loss: 0.0428 - accuracy: 0.0000e+00
Epoch 20/20
20/20 [==============================] - 7s 348ms/step - loss: 0.0428 - accuracy: 0.0000e+00
*TEST*
1800/1800 [==============================] - 3s 2ms/step - loss: 0.0449 - accuracy: 0.0000e+00
accuracy: 0%
我的 input_shape 是 (4, 256)，我的训练数据数组的形状是 (57600, 4, 256)，这意味着我有 57600 个形状 (4,256) 的样本。我还有我的训练标签数组（我应该从数据中获得的值），其形状为 (57600,)。最后，我使用的库是 TENSORFLOW。
我的代码是下一个
from keras.layers import Input, Dense, concatenate, Conv2D, MaxPooling2D, Flatten
from keras.models import Model
from tensorflow import keras
from sklearn.preprocessing import MinMaxScaler
div_n = 240
#DIVIDING THE DATA WE WANT TO CLASIFFY AND ITS LABELS - It is being used the
#scaled data
data = np.array([self_mbyy_scaled,
self_mvyy_scaled,
self_mtpr_scaled,
self_mrho_scaled])
labels = self_iout_scaled
print(np.shape(data))
print(np.shape(labels))
#TRAINING SET AND DATA SET
tr_data = []
tr_labels = []
#Here I'm dividing the whole data in half for the nx, nz dimensions. The first half is the training set and the second half is the test set
for j in range(div_n):
for k in range(div_n):
tr_data.append([data[0][j,:,k],
data[1][j,:,k],
data[2][j,:,k],
data[3][j,:,k]]) #It puts the magnetic field, velocity, temperature and density values in one row for 240x240=57600 columns
tr_labels.append(labels[j,k]) #the values from the column of targets
tr_data = np.array(tr_data)
tr_data = tr_data.reshape(div_n*div_n, len(data), self_ny, 1)
tr_labels = np.array(tr_labels)
print('\n training data shape')
print(np.shape(tr_data))
print('\n training labels shape')
print(np.shape(tr_labels))
te_data = []
te_labels = []
for j in range(div_n):
for k in range(div_n):
te_data.append([data[0][div_n+j,:,div_n+k],
data[1][div_n+j,:,div_n+k],
data[2][div_n+j,:,div_n+k],
data[3][div_n+j,:,div_n+k]]) #It puts the magnetic field, velocity, temperature and density values in one row for 240x240=57600 columns
te_labels.append(labels[div_n+j,div_n+k]) #the values from the column of targets
te_data = np.array(te_data)
te_data = te_data.reshape(div_n*div_n, len(data), self_ny, 1)
te_labels = np.array(te_labels)
print('\n test data shape')
print(np.shape(te_data))
print('\n test labels shape')
print(np.shape(te_labels))
print('\n')
#NEURAL NETWORK MODEL
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(4, 256, 1)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(1))
model.summary()
model.compile(
optimizer=keras.optimizers.Adam(0.001),
loss=keras.losses.MeanSquaredError(),
metrics=['accuracy'],
)
model.fit(
tr_data, tr_labels,
epochs=6,
validation_data=ds_valid,
)
由于您的数据似乎具有空间维度 57600, 4, 256 --> (samples, timesteps, features)，我建议使用 Conv1D 层而不是 Conv2D。这是一个简单的工作示例：
import tensorflow as tf
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv1D(128, 2, activation='relu', input_shape=(4, 256)))
model.add(tf.keras.layers.Conv1D(64, 2, activation='relu'))
model.add(tf.keras.layers.Conv1D(32, 2, activation='relu'))
model.add(tf.keras.layers.GlobalMaxPool1D())
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(1))
model.summary()
model.compile(
optimizer=tf.keras.optimizers.Adam(0.001),
loss=tf.keras.losses.MeanSquaredError(),
metrics=['mse'],
)
samples = 50
x = tf.random.normal((50, 4, 256))
y = tf.random.normal((50,))
model.fit(x, y, batch_size=10, epochs=6)
请注意，您通常不会将 accuracy 指标用于 tf.keras.losses.MeanSquaredError 损失函数。
我有这段代码，我试图在其中拟合一个只有三层的神经网络模型：输入层、隐藏层以及最后必须只有一个神经元的输出层对于单个输出。问题是，在进行拟合时，我总是获得相同的准确度值（空值）和损失值（保持不变），并且我尝试将优化器从 'sgd' 更改为 'adam'仍然一切正常。你会推荐什么？
Layer (type) Output Shape Param N°
=================================================================
data_in (InputLayer) [(None, 4, 256)] 0
dense (Dense) (None, 4, 124) 31868
dense_1 (Dense) (None, 4, 1) 125
=================================================================
Total params: 31,993
Trainable params: 31,993
Non-trainable params: 0
_________________________________________________________________
Epoch 1/20
20/20 [==============================] - 8s 350ms/step - loss: 0.3170 - accuracy: 1.7361e-05
Epoch 2/20
20/20 [==============================] - 7s 348ms/step - loss: 0.2009 - accuracy: 6.7817e-08
Epoch 3/20
20/20 [==============================] - 7s 348ms/step - loss: 0.0513 - accuracy: 0.0000e+00
Epoch 4/20
20/20 [==============================] - 7s 348ms/step - loss: 0.0437 - accuracy: 0.0000e+00
Epoch 5/20
20/20 [==============================] - 7s 346ms/step - loss: 0.0430 - accuracy: 0.0000e+00
Epoch 6/20
20/20 [==============================] - 7s 346ms/step - loss: 0.0428 - accuracy: 0.0000e+00
Epoch 7/20
20/20 [==============================] - 7s 345ms/step - loss: 0.0428 - accuracy: 0.0000e+00
Epoch 8/20
20/20 [==============================] - 7s 345ms/step - loss: 0.0430 - accuracy: 0.0000e+00
Epoch 9/20
20/20 [==============================] - 7s 348ms/step - loss: 0.0429 - accuracy: 0.0000e+00
Epoch 10/20
20/20 [==============================] - 7s 348ms/step - loss: 0.0429 - accuracy: 0.0000e+00
Epoch 11/20
20/20 [==============================] - 7s 346ms/step - loss: 0.0429 - accuracy: 0.0000e+00
Epoch 12/20
20/20 [==============================] - 7s 344ms/step - loss: 0.0428 - accuracy: 0.0000e+00
Epoch 13/20
20/20 [==============================] - 7s 348ms/step - loss: 0.0428 - accuracy: 0.0000e+00
Epoch 14/20
20/20 [==============================] - 7s 345ms/step - loss: 0.0433 - accuracy: 0.0000e+00
Epoch 15/20
20/20 [==============================] - 7s 345ms/step - loss: 0.0430 - accuracy: 0.0000e+00
Epoch 16/20
20/20 [==============================] - 7s 347ms/step - loss: 0.0432 - accuracy: 0.0000e+00
Epoch 17/20
20/20 [==============================] - 7s 346ms/step - loss: 0.0429 - accuracy: 0.0000e+00
Epoch 18/20
20/20 [==============================] - 7s 347ms/step - loss: 0.0430 - accuracy: 0.0000e+00
Epoch 19/20
20/20 [==============================] - 7s 348ms/step - loss: 0.0428 - accuracy: 0.0000e+00
Epoch 20/20
20/20 [==============================] - 7s 348ms/step - loss: 0.0428 - accuracy: 0.0000e+00
*TEST*
1800/1800 [==============================] - 3s 2ms/step - loss: 0.0449 - accuracy: 0.0000e+00
accuracy: 0%
我的 input_shape 是 (4, 256)，我的训练数据数组的形状是 (57600, 4, 256)，这意味着我有 57600 个形状 (4,256) 的样本。我还有我的训练标签数组（我应该从数据中获得的值），其形状为 (57600,)。最后，我使用的库是 TENSORFLOW。
我的代码是下一个
from keras.layers import Input, Dense, concatenate, Conv2D, MaxPooling2D, Flatten
from keras.models import Model
from tensorflow import keras
from sklearn.preprocessing import MinMaxScaler
div_n = 240
#DIVIDING THE DATA WE WANT TO CLASIFFY AND ITS LABELS - It is being used the
#scaled data
data = np.array([self_mbyy_scaled,
self_mvyy_scaled,
self_mtpr_scaled,
self_mrho_scaled])
labels = self_iout_scaled
print(np.shape(data))
print(np.shape(labels))
#TRAINING SET AND DATA SET
tr_data = []
tr_labels = []
#Here I'm dividing the whole data in half for the nx, nz dimensions. The first half is the training set and the second half is the test set
for j in range(div_n):
for k in range(div_n):
tr_data.append([data[0][j,:,k],
data[1][j,:,k],
data[2][j,:,k],
data[3][j,:,k]]) #It puts the magnetic field, velocity, temperature and density values in one row for 240x240=57600 columns
tr_labels.append(labels[j,k]) #the values from the column of targets
tr_data = np.array(tr_data)
tr_data = tr_data.reshape(div_n*div_n, len(data), self_ny, 1)
tr_labels = np.array(tr_labels)
print('\n training data shape')
print(np.shape(tr_data))
print('\n training labels shape')
print(np.shape(tr_labels))
te_data = []
te_labels = []
for j in range(div_n):
for k in range(div_n):
te_data.append([data[0][div_n+j,:,div_n+k],
data[1][div_n+j,:,div_n+k],
data[2][div_n+j,:,div_n+k],
data[3][div_n+j,:,div_n+k]]) #It puts the magnetic field, velocity, temperature and density values in one row for 240x240=57600 columns
te_labels.append(labels[div_n+j,div_n+k]) #the values from the column of targets
te_data = np.array(te_data)
te_data = te_data.reshape(div_n*div_n, len(data), self_ny, 1)
te_labels = np.array(te_labels)
print('\n test data shape')
print(np.shape(te_data))
print('\n test labels shape')
print(np.shape(te_labels))
print('\n')
#NEURAL NETWORK MODEL
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(4, 256, 1)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(1))
model.summary()
model.compile(
optimizer=keras.optimizers.Adam(0.001),
loss=keras.losses.MeanSquaredError(),
metrics=['accuracy'],
)
model.fit(
tr_data, tr_labels,
epochs=6,
validation_data=ds_valid,
)
由于您的数据似乎具有空间维度 57600, 4, 256 --> (samples, timesteps, features)，我建议使用 Conv1D 层而不是 Conv2D。这是一个简单的工作示例：
import tensorflow as tf
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv1D(128, 2, activation='relu', input_shape=(4, 256)))
model.add(tf.keras.layers.Conv1D(64, 2, activation='relu'))
model.add(tf.keras.layers.Conv1D(32, 2, activation='relu'))
model.add(tf.keras.layers.GlobalMaxPool1D())
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(1))
model.summary()
model.compile(
optimizer=tf.keras.optimizers.Adam(0.001),
loss=tf.keras.losses.MeanSquaredError(),
metrics=['mse'],
)
samples = 50
x = tf.random.normal((50, 4, 256))
y = tf.random.normal((50,))
model.fit(x, y, batch_size=10, epochs=6)
请注意，您通常不会将 accuracy 指标用于 tf.keras.losses.MeanSquaredError 损失函数。