title: My model produces 1.0 training accuracy and validation accuracy, but the predicted labels are incorrect
body: <p>I am using the model:</p>
  <pre><code>dim = (94, 1)
  x_input = tf.keras.Input(shape=dim)
  x = Conv1D(10, kernel_size=2, padding='same', activation='relu')(x_input)
  x = MaxPooling1D(2, padding='same')(x)
  x = Conv1D(24, kernel_size=2, padding='same', activation='relu')(x)
  x = MaxPooling1D(2, padding='same')(x)
  x = Conv1D(64, kernel_size=2, padding='same', activation='relu', kernel_initializer='lecun_uniform', bias_initializer='zeros')(x)
  x = Flatten()(x)
  x = Dense(16, activation=&quot;softmax&quot;)(x)
  model = tf.keras.models.Model(inputs=[x_input], outputs=[x])
  model.compile(optimizer=&quot;Adam&quot;, loss='binary_crossentropy', metrics=['accuracy'])
  </code></pre>
  <p>This model, when using my training data produces a 1.0 accuracy and 1.0 for testing accuracy. When I use the test data and model.predict, I dont receive the correct labels. Specifically, the labels are constant for the entire duration.</p>
  <p>When I use model.evaluate the model correctly predicts the labels, but when using evaluate it doesn't.
  the input is 7500 entries of (94,1) with 7500 outputs of (16,1). Is there an issue with the model?</p>