Код: Выделить всё
import numpy as np
import pandas as pd
import tensorflow as tf
import numpy as np
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Embedding, GlobalAveragePooling1D
from tensorflow.keras.layers import TextVectorization
from sklearn.model_selection import train_test_split
from tensorflow import keras
from nltk.tokenize.treebank import TreebankWordTokenizer, TreebankWordDetokenizer
from sklearn.feature_extraction.text import CountVectorizer
dataf=pd.read_csv('D:/datafile.csv')
data=pd.read_csv("D:/dataset1c2f4b7/dataset/train.csv",encoding='latin-1')
l=[]
for a in dataf['text']:
l.append(a)
m=[]
for a in dataf['target']:
m.append(a)
X_train, X_test, y_train, y_test = train_test_split(l, m, test_size=0.2, random_state=42)
vectorizer = CountVectorizer()
vectorizer.fit(X_train)
X_train = vectorizer.transform(X_train)
X_test = vectorizer.transform(X_test)
X_train=np.array(X_train)
X_test=np.array(X_test)
y_train=np.array(y_train)
y_test=np.array(y_test)
print(X_train)
model = keras.models.Sequential()
model.add(keras.layers.Embedding(10000, 128))
model.add(keras.layers.SimpleRNN(64, return_sequences=True))
model.add(keras.layers.SimpleRNN(64))
model.add(keras.layers.Dense(128, activation="relu"))
model.add(keras.layers.Dropout(0.4))
model.add(keras.layers.Dense(1, activation="sigmoid"))
model.summary()
model.compile("rmsprop",
"binary_crossentropy",
metrics=["accuracy"])
model.fit(X_train, y_train,epochs=5,verbose=False,validation_data=(X_test, y_test),batch_size=10)
model.save('gfgModel.h5')
tf.saved_model.save(model, 'one_step 05')
Код: Выделить всё
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type csr_matrix)
Я просто ожидал, что модель будет обучена, поскольку все находится в форме массива.
Подробнее здесь: https://stackoverflow.com/questions/790 ... bject-type