Я тренирую набор данных для обзора продуктов питания Amazon. Код, который я написал для него, показывает ошибку как-
Encoder_Decoder LSTM ... /opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:66: UserWarning: Обновите свой LSTM вызов API Keras 2: LSTM(10, return_sequences=True, return_state=True, dropout=0.2, recurrent_dropout=0.2) /opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:67: UserWarning: Обновите ваш LSTM вызов API Keras 2: ` LSTM (10, return_state = True, return_sequence = True, go_backwards = True, dropo
Вход 0 несовместим со слоем lstm_3: ожидаемый ndim = 3, найденный ndim = 1
Какая часть код точно говорит об ошибке в?
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
# Any results you write to the current directory are saved as output.
!pip install pyrouge
import re
from nltk.corpus import stopwords
from numpy.random import seed
seed(1)
from sklearn.model_selection import train_test_split as tts
import logging
from pyrouge.Rouge155 import Rouge155
import matplotlib.pyplot as plt
import keras
from keras import backend as k
k.set_learning_phase(1)
from keras import initializers
from keras.optimizers import RMSprop
from keras.models import Model
from keras.layers import Dense,LSTM,Input,Activation,Add,TimeDistributed,\
Permute,Flatten,RepeatVector,merge,Lambda,Multiply,Reshape
from keras.callbacks import ModelCheckpoint
from keras.models import load_model
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',\
level=logging.INFO)
#model parameters
batch_size = 50
num_classes = 1
epochs = 20
hidden_units = 10
learning_rate = 0.005
clip_norm = 2.0
train_data=pd.read_csv("../input/Text Summarization Dataset.csv")
en_shape=np.shape(train_data["Text"][0])
de_shape=np.shape(train_data["Summary"][0])
#Helper Functions
def encoder_decoder(data):
print('Encoder_Decoder LSTM...')
#Encoder
encoder_inputs = Input(shape=en_shape)
encoder_LSTM = LSTM(hidden_units,dropout_U=0.2,dropout_W=0.2,return_sequences=True,return_state=True)
encoder_LSTM_rev=LSTM(hidden_units,return_state=True,return_sequences=True,dropout_U=0.05,dropout_W=0.05,go_backwards=True)
encoder_outputs, state_h, state_c = encoder_LSTM(encoder_inputs)
encoder_outputsR, state_hR, state_cR = encoder_LSTM_rev(encoder_inputs)
state_hfinal=Add()([state_h,state_hR])
state_cfinal=Add()([state_c,state_cR])
encoder_outputs_final = Add()([encoder_outputs,encoder_outputsR])
encoder_states = [state_hfinal,state_cfinal]
#decoder
decoder_inputs = Input(shape=(None,de_shape[1]))
decoder_LSTM = LSTM(hidden_units,return_sequences=True,dropout_U=0.2,dropout_W=0.2,return_state=True)
decoder_outputs, _, _ = decoder_LSTM(decoder_inputs,initial_state=encoder_states)
#Pull out XGBoost, (I mean attention)
attention = TimeDistributed(Dense(1, activation = 'tanh'))(encoder_outputs_final)
attention = Flatten()(attention)
attention = Multiply()([decoder_outputs, attention])
attention = Activation('softmax')(attention)
attention = Permute([2, 1])(attention)
decoder_dense = Dense(de_shape[1],activation='softmax')
decoder_outputs = decoder_dense(attention)
m
model= Model(inputs=[encoder_inputs,decoder_inputs], outputs=decoder_outputs)
print(model.summary())
rmsprop = RMSprop(lr=learning_rate,clipnorm=clip_norm)
model.compile(loss='categorical_crossentropy',optimizer=rmsprop,metrics=['accuracy'])
x_train,x_test,y_train,y_test=tts(data["Text"],data["Summary"],test_size=0.20)
history= model.fit(x=[x_train,y_train],
y=y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=([x_test,y_test], y_test))
print(model.summary())
#inference mode
encoder_model_inf = Model(encoder_inputs,encoder_states)
decoder_state_input_H = Input(shape=(en_shape[0],))
decoder_state_input_C = Input(shape=(en_shape[0],))
decoder_state_inputs = [decoder_state_input_H, decoder_state_input_C]
decoder_outputs, decoder_state_h, decoder_state_c = decoder_LSTM(decoder_inputs,
initial_state=decoder_state_inputs)
decoder_states = [decoder_state_h, decoder_state_c]
decoder_outputs = decoder_dense(decoder_outputs)
decoder_model_inf= Model([decoder_inputs]+decoder_state_inputs,
[decoder_outputs]+decoder_states)
scores = model.evaluate([x_test,y_test],y_test, verbose=1)
print('LSTM test scores:', scores)
print('\007')
print(model.summary())
return model,encoder_model_inf,decoder_model_inf,history
#generate summary from vectors
def generateText(SentOfVecs):
SentOfVecs=np.reshape(SentOfVecs,de_shape)
kk=""
for k in SentOfVecs:
kk = kk + label_encoder.inverse_transform([argmax(k)])[0].strip()+" "
#kk=kk+((getWord(k)[0]+" ") if getWord(k)[1]>0.01 else "")
return kk
"""generate summary vectors"""
def summarize(article):
stop_pred = False
article = np.reshape(article,(1,en_shape[0],en_shape[1]))
#get initial h and c values from encoder
init_state_val = encoder.predict(article)
target_seq = np.zeros((1,1,de_shape[1]))
#target_seq =np.reshape(train_data['summaries'][k][0],(1,1,de_shape[1]))
generated_summary=[]
while not stop_pred:
decoder_out,decoder_h,decoder_c= decoder.predict(x=[target_seq]+init_state_val)
generated_summary.append(decoder_out)
init_state_val= [decoder_h,decoder_c]
#get most similar word and put in line to be input in next timestep
#target_seq=np.reshape(model.wv[getWord(decoder_out)[0]],(1,1,emb_size_all))
target_seq=np.reshape(decoder_out,(1,1,de_shape[1]))
if len(generated_summary)== de_shape[0]:
stop_pred=True
break
return generated_summary
#Plot training curves
def plot_training(history):
print(history.history.keys())
# "Accuracy"
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
# "Loss"
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
def saveModels():
trained_model.save("%sinit_model"%modelLocation)
encoder.save("%sencoder"%modelLocation)
decoder.save("%sdecoder"%modelLocation)
def evaluate_summ(article):
ref=''
for k in wt(data['Summary'][article])[:20]:
ref=ref+' '+k
gen_sum = generateText(summarize(train_data["Text"][article]))
print("-----------------------------------------------------")
print("Original summary")
print(ref)
print("-----------------------------------------------------")
print("Generated summary")
print(gen_sum)
print("-----------------------------------------------------")
rouge = Rouge155()
score = rouge.score_summary(ref, gen_sum)
print("Rouge1 Score: ", score)
#Train model and test
trained_model,encoder,decoder,history = encoder_decoder(train_data)
plot_training(history)
evaluate_summ(10)
print(generateText(summarize(train_data["article"][8])))
print(data["Summary"][8])
print(data["Text"][78])