title: How can I see the scores of every class after run the predict_classes
body: <p>I have a classified network for the MNIST dataset (csv) with 10 labels which are numbers (0,1,2,3,4,5,6,7,8,9) and after training the network, I run the predict_classes for test_data. I want to know for each of the data in test_set what is the score of every label(0,1,2,3,4,5,6,7,8,9) in y_pred.
  for example if predict_classes say that for first data the label is &quot;7&quot; what is the score of 7 and what is the scores of other labels such (0,1,2,3,4,5,6,8,9)
  How can I write its code?</p>
  <pre><code>from keras import models 
  import numpy as np
  from keras import layers
  import tensorflow as  tf
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense, Dropout, LSTM, BatchNormalization
  mnist = tf.keras.datasets.mnist
  #Load dataset
  (x_train, y_train), (x_test, y_test) = mnist.load_data()
  #normalizing data
  x_train = x_train / 255.0
  x_test = x_test / 255.0
  
  # bulid model
  model = Sequential()
  model.add(LSTM(15, input_shape=(x_train.shape[1:]), return_sequences=True, activation=&quot;tanh&quot;, unroll=True))
  model.add(LSTM(15, dropout=0.2, activation=&quot;tanh&quot;, unroll=True))
  #model.add(LSTM(1, activation='tanh'))
  #model.add(LSTM(2, activation='tanh'))
  model.add(Dense(5, activation='tanh' ))
  model.add(Dense(10, activation='sigmoid'))
  model.summary()
  opt = tf.keras.optimizers.Adam(lr=1e-3, decay=1e-5)
  model.compile(loss='sparse_categorical_crossentropy', optimizer=opt,
               metrics=['accuracy'])
  model.fit(x_train, y_train, epochs=2, validation_data=(x_test, y_test))
  
  
  y_pred = model.predict_classes(x_test)
  print(y_pred)
  </code></pre>