[英]WARNING:tensorflow:Model was constructed with shape (20, 37, 42) for input Tensor(“input_5:0”, shape=(20, 37, 42), dtype=float32), but
WARNING:tensorflow:Model was constructed with shape (20, 37, 42) for input Tensor("input_5:0", shape=(20, 37, 42), dtype=float32), but it was called on an input with incompatible shape (None, 37).警告:tensorflow:模型是用形状 (20, 37, 42) 构建的,用于输入 Tensor("input_5:0", shape=(20, 37, 42), dtype=float32),但在不兼容的输入上调用它形状(无,37)。
Hello!你好! Deep learning noob here... I'm having trouble using LSTM layers.这里是深度学习菜鸟……我在使用 LSTM 层时遇到了问题。 The input is a length 37 float array containing 2 floats and a length 35 one-hot array converted into float.输入是一个长度为 37 的浮点数组,包含 2 个浮点数和一个长度为 35 的单热数组转换为浮点数。 The output is a length 19 array with 0s and 1s.输出是一个长度为 19 的数组,包含 0 和 1。 Like the title suggests, I'm having trouble reshaping my input data to fit the model, and I'm not even sure what input dimensions would be considered 'compatible'就像标题所暗示的那样,我在重塑我的输入数据以适应模型时遇到了麻烦,我什至不确定哪些输入维度会被认为是“兼容的”
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import random
inputs, outputs = [], []
for x in range(10000):
tempi, tempo = [], []
tempi.append(random.random() - 0.5)
tempi.append(random.random() - 0.5)
for x2 in range(35):
if random.random() > 0.5:
tempi.append(1.)
else:
tempi.append(0.)
for x2 in range(19):
if random.random() > 0.5:
tempo.append(1.)
else:
tempo.append(0.)
inputs.append(tempi)
outputs.append(tempo)
batch = 20
timesteps = 42
training_units = 0.85
cutting_point_i = int(len(inputs)*training_units)
cutting_point_o = int(len(outputs)*training_units)
x_train, x_test = np.asarray(inputs[:cutting_point_i]), np.asarray(inputs[cutting_point_i:])
y_train, y_test = np.asarray(outputs[:cutting_point_o]), np.asarray(outputs[cutting_point_o:])
input_layer = keras.Input(shape=(37,timesteps),batch_size=batch)
dense = layers.LSTM(150, activation="sigmoid", return_sequences=True)
x = dense(input_layer)
hidden_layer_2 = layers.LSTM(150, activation="sigmoid", return_sequences=True)(x)
output_layer = layers.Dense(10, activation="softmax")(hidden_layer_2)
model = keras.Model(inputs=input_layer, outputs=output_layer, name="my_model"
Several problems here.这里有几个问题。
(n, time steps, features)
您的输入没有时间步长,您需要输入形状(n, time steps, features)
input_shape
, the time steps dimension comes first, not last在input_shape
,时间步长维度首先出现,而不是最后What I did:我做了什么:
input_shape
我置换了input_shape
的维度return_sequences=False
我设置了最终的return_sequences=False
Completely fixed example with generated data:具有生成数据的完全固定示例:
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
batch = 20
n_samples = 1000
timesteps = 7
features = 10
x_train = np.random.rand(n_samples, timesteps, features)
y_train = keras.utils.to_categorical(np.random.randint(0, 10, n_samples))
input_layer = keras.Input(shape=(timesteps, features),batch_size=batch)
dense = layers.LSTM(16, activation="sigmoid", return_sequences=True)(input_layer)
hidden_layer_2 = layers.LSTM(16, activation="sigmoid", return_sequences=False)(dense)
output_layer = layers.Dense(10, activation="softmax")(hidden_layer_2)
model = keras.Model(inputs=input_layer, outputs=output_layer, name="my_model")
model.compile(loss='categorical_crossentropy', optimizer='adam')
history = model.fit(x_train, y_train)
Train on 1000 samples
20/1000 [..............................] - ETA: 2:50 - loss: 2.5145
200/1000 [=====>........................] - ETA: 14s - loss: 2.3934
380/1000 [==========>...................] - ETA: 5s - loss: 2.3647
560/1000 [===============>..............] - ETA: 2s - loss: 2.3549
740/1000 [=====================>........] - ETA: 1s - loss: 2.3395
900/1000 [==========================>...] - ETA: 0s - loss: 2.3363
1000/1000 [==============================] - 4s 4ms/sample - loss: 2.3353
The correct input for your model is (20, 37, 42).模型的正确输入是 (20, 37, 42)。 Note: Here 20 is the batch_size you have explicitly specified.注意:这里 20 是您明确指定的 batch_size。
Code:代码:
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
batch = 20
timesteps = 42
training_units = 0.85
x1 = tf.constant(np.random.randint(50, size =(1000,37, 42)), dtype = tf.float32)
y1 = tf.constant(np.random.randint(10, size =(1000,)), dtype = tf.int32)
input_layer = keras.Input(shape=(37,timesteps),batch_size=batch)
dense = layers.LSTM(150, activation="sigmoid", return_sequences=True)
x = dense(input_layer)
hidden_layer_2 = layers.LSTM(150, activation="sigmoid", return_sequences=True)(x)
hidden_layer_3 = layers.Flatten()(hidden_layer_2)
output_layer = layers.Dense(10, activation="softmax")(hidden_layer_3)
model = keras.Model(inputs=input_layer, outputs=output_layer, name="my_model")
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
tf.keras.utils.plot_model(model, 'my_first_model.png', show_shapes=True)
Model Architecture:模型架构:
You can clearly see the Input Size.您可以清楚地看到输入大小。
Code to Run:要运行的代码:
model.fit(x = x1, y = y1, batch_size = batch, epochs = 10)
Note: Whatever batch_size you have specified you have to specify the same batch_size in the model.fit() command.注意:无论您指定了什么 batch_size,您都必须在 model.fit() 命令中指定相同的 batch_size。
Output:输出:
Epoch 1/10
50/50 [==============================] - 4s 89ms/step - loss: 2.3288 - accuracy: 0.0920
Epoch 2/10
50/50 [==============================] - 5s 91ms/step - loss: 2.3154 - accuracy: 0.1050
Epoch 3/10
50/50 [==============================] - 5s 101ms/step - loss: 2.3114 - accuracy: 0.0900
Epoch 4/10
50/50 [==============================] - 5s 101ms/step - loss: 2.3036 - accuracy: 0.1060
Epoch 5/10
50/50 [==============================] - 5s 99ms/step - loss: 2.2998 - accuracy: 0.1000
Epoch 6/10
50/50 [==============================] - 4s 89ms/step - loss: 2.2986 - accuracy: 0.1170
Epoch 7/10
50/50 [==============================] - 4s 84ms/step - loss: 2.2981 - accuracy: 0.1300
Epoch 8/10
50/50 [==============================] - 5s 103ms/step - loss: 2.2950 - accuracy: 0.1290
Epoch 9/10
50/50 [==============================] - 5s 106ms/step - loss: 2.2960 - accuracy: 0.1210
Epoch 10/10
50/50 [==============================] - 5s 97ms/step - loss: 2.2874 - accuracy: 0.1210
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