title: expected dense to have shape but got array with shape
body: <p>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.</p>
  
  <pre><code>ValueError: Error when checking input: expected dense_1_input to have shape (100,) but got array with shape (1,)
  </code></pre>
  
  <p>My data has 5 classes and has a total of 15 examples only. Below is the dataset</p>
  
  <pre><code>             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
  </code></pre>
  
  <p>This is the code of my model</p>
  
  <pre><code>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)
  </code></pre>
  
  <p>I am not able to figure out where the problem lies and how to fix it.</p>