title: Cannot train a model with numpy array
body: <p>I am training the following autoencoder on float numbers. </p>
  
  <pre><code>input_img = Input(shape=(2623,1), name='input')
  
  x = ZeroPadding1D(1)(input_img)
  x = Conv1D(32, 3, activation='relu', padding='same', use_bias=False)(input_img)
  x = BatchNormalization(axis=-1)(x)
  x = MaxPooling1D(2, padding='same')(x)
  x = Conv1D(16, 3, activation='relu', padding='same', use_bias=False)(x)
  x = BatchNormalization(axis=-1)(x)
  x = MaxPooling1D(2, padding='same')(x)
  x = Conv1D(16,3, activation='relu', padding='same', use_bias=False)(x)
  x = BatchNormalization(axis=-1)(x)
  encoded = MaxPooling1D(2, padding='same')(x)
  
  
  x = Conv1D(16,3, activation='relu', padding='same', use_bias=False)(encoded)
  x = BatchNormalization(axis=-1)(x)
  x = UpSampling1D(2)(x)
  x = Conv1D(16,3, activation='relu', padding='same', use_bias=False)(x)
  x = BatchNormalization(axis=-1)(x)
  x = UpSampling1D(2)(x)
  x = Conv1D(32, 3, activation='relu', padding='same', use_bias=False)(x)  #input_shape=(30, 1))
  x = BatchNormalization(axis=-1)(x)  
  x = UpSampling1D(2)(x)
  x = Cropping1D(cropping=(0, 1))(x) #Crop nothing from input but crop 1 element from the end
  decoded = Conv1D(1, 3, activation='sigmoid', padding='same', use_bias=False)(x)  
  
  autoencoder = Model(input_img, decoded)
  autoencoder.compile(optimizer='rmsprop', loss='binary_crossentropy')
  
  
  x = Input(shape=(16, 300), name="input")
  h = x
  h = Conv1D(filters=300, kernel_size=16,
               activation="relu", padding='same', name='Conv1')(h)
  h = MaxPooling1D(pool_size=16, name='Maxpool1')(h)
  </code></pre>
  
  <p>I had to convert the data to a numpy array in order to process it but when the model starts training i get:</p>
  
  <blockquote>
    <p>ValueError: could not convert string to float:</p>
  </blockquote>
  
  <p>This is happening because my training data looks like :</p>
  
  <blockquote>
    <p>train[0][1] # one number of training data</p>
  </blockquote>
  
  <pre><code>array(['0.001758873'], dtype=object)
  </code></pre>
  
  <p>What can i do in order to avoid the "dtype=object" in my training data, or maybe do i have to convert it to something else?
  Thank you!</p>