我是Keras的新手,我正在尝试建立个人使用/未来学习的模型。我刚开始使用python,并想出了这段代码(在视频和教程的帮助下)。我有16324个实例的数据,每个实例包含18个特征和1个因变量。
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import pandas as pd
import os
import time
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM, BatchNormalization
from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint
EPOCHS = 10
BATCH_SIZE = 64
NAME = f"-TEST-{int(time.time())}"
df = pd.read_csv("EntryData.csv", names=['1SH5', '1SHA', '1SA5', '1SAA', '1WH5', '1WHA', '2SA5', '2SAA', '2SH5', '2SHA', '2WA5', '2WAA', '3R1', '3R2', '3R3', '3R4', '3R5', '3R6', 'Target'])
df_val = 14554
validation_df = df[df.index > df_val]
df = df[df.index <= df_val]
train_x = df.drop(columns=['Target'])
train_y = df[['Target']]
validation_x = validation_df.drop(columns=['Target'])
validation_y = validation_df[['Target']]
model = Sequential()
model.add(LSTM(128, input_shape=(train_x.shape[1:]), return_sequences=True))
model.add(Dropout(0.2))
model.add(BatchNormalization())
model.add(LSTM(128, return_sequences=True))
model.add(Dropout(0.1))
model.add(BatchNormalization())
model.add(LSTM(128))
model.add(Dropout(0.2))
model.add(BatchNormalization())
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(2, activation='softmax'))
opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)
model.compile(loss='sparse_categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
tensorboard = TensorBoard(log_dir=f'logs/{NAME}')
filepath = "RNN_Final-{epoch:02d}-{val_acc:.3f}"
checkpoint = ModelCheckpoint("models/{}.model".format(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')) # saves only the best ones
history = model.fit(
train_x, train_y,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_data=(validation_x, validation_y),
callbacks=[tensorboard, checkpoint],)
score = model.evaluate(validation_x, validation_y, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
model.save("models/{}".format(NAME))
抛出错误:
ValueError:lstm层的输入0与该层不兼容: 预期ndim = 3,找到的ndim = 2。收到完整的图形:[无,18]
我已经在这个网站和google上搜索解决方案已有几个小时了,但我无法为此找到合适的答案,或者我也无法针对类似问题实施解决方案。
谢谢您的提示。
答案 0 :(得分:1)
一个LSTM网络需要一个三维输入。这是对我有用的代码:
在将其馈送到神经网络之前:
X_train = X_train.reshape(X_train.shape[0], 1, X_train.shape[1])
在神经网络中:
LSTM(64, input_shape=(X_train.shape[1:])),