could you help me for a bit ? this is what i did to run the prediction.
import pandas as pd
import numpy as np
import tensorflow as tf
import tensorflow.keras as keras
text preprocessing
from nltk.tokenize import word_tokenize
import re
plots and metrics
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score, f1_score, confusion_matrix
preparing input to our model
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical
keras layers
from keras.models import Sequential
from tensorflow.keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense
from keras.models import load_model
could you help me for a bit ? this is what i did to run the prediction.
import pandas as pd import numpy as np import tensorflow as tf import tensorflow.keras as keras
text preprocessing
from nltk.tokenize import word_tokenize import re
plots and metrics
import matplotlib.pyplot as plt from sklearn.metrics import accuracy_score, f1_score, confusion_matrix
preparing input to our model
from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.utils import to_categorical
keras layers
from keras.models import Sequential from tensorflow.keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense from keras.models import load_model
import time
max_seq_len = 500
tokenizer = Tokenizer()
message = ['i am so sad!']
class_names = ['joy', 'fear', 'anger', 'sadness', 'neutral']
model= load_model('models/cnn_w2v.h5')
seq = tokenizer.texts_to_sequences(message) padded = pad_sequences(seq, maxlen=max_seq_len)
start_time = time.time() pred = model.predict(padded)
print('Message: ' + str(message)) print('predicted: {} ({:.2f} seconds)'.format(class_names[np.argmax(pred)], (time.time() - start_time)))