expected dense to have shape but got array with shape

Question

I am getting the following error while calling the model.predict function when running a text classification model in keras. I searched the everywhere but it isn't working for me.

ValueError: Error when checking input: expected dense_1_input to have shape (100,) but got array with shape (1,)

My data has 5 classes and has a total of 15 examples only. Below is the dataset

             query        tags
0               hi       intro
1      how are you       wellb
2            hello       intro
3        what's up       wellb
4       how's life       wellb
5              bye          gb
6    see you later          gb
7         good bye          gb
8           thanks   gratitude
9        thank you   gratitude
10  that's helpful   gratitude
11      I am great  revertfine
12            fine  revertfine
13       I am fine  revertfine
14            good  revertfine

This is the code of my model

from keras.preprocessing.text import Tokenizer
from sklearn.preprocessing import LabelBinarizer
from keras.models import Sequential
import pandas as pd
from keras.layers import Dense, Activation

data = pd.read_csv('text_class.csv')
train_text = data['query']
train_labels = data['tags']

tokenize = Tokenizer(num_words=100)
tokenize.fit_on_texts(train_text)

x_data = tokenize.texts_to_matrix(train_text)

encoder = LabelBinarizer()
encoder.fit(train_labels)
y_data = encoder.transform(train_labels)

model = Sequential()
model.add(Dense(512, input_shape=(100,)))
model.add(Activation('relu'))
model.add(Dense(5))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
model.fit(x_data, y_data, batch_size=8, epochs=10)

predictions = model.predict(x_data[0])
tag_labels = encoder.classes_
predicted_tags = tag_labels[np.argmax(predictions)]
print (predicted_tags)

I am not able to figure out where the problem lies and how to fix it.

Answer 1

x_data is 2-dimensional array with shape (15, 100)

  print(x_data.shape) 

but x_data[0] is 1-dimensional array with shape (100, )

  print(x_data[0].shape) 

and it makes problem.

Use slicing x_data[0:1] to get it as 2-dimensional array with shape (1, 100)

 print(x_data[0:1].shape) 

and it will work

 predictions = model.predict(x_data[0:1])