Styjun

I'm implementing a regression model in tensorflow DNN framework which takes input of shape (1504924, 127) and predicts the value corresponding (of shape (1504924,1).

Following is my network

class q_model:
 def __init__(self, 
 sess, 
 quantiles, 
 in_shape=127, 
 out_shape=1, 
 batch_size=32):

 self.sess = sess

 self.quantiles = quantiles
 self.num_quantiles = len(quantiles)

 self.in_shape = in_shape
 self.out_shape = out_shape
 self.batch_size = batch_size

 self.outputs = []
 self.losses = []
 self.loss_history = []

 self.build_model()
 print("Completed")

 def build_model(self, scope='q_model', reuse=tf.AUTO_REUSE): 
 with tf.variable_scope(scope, reuse=reuse) as scope:
 self.x = tf.placeholder(tf.float32, shape=(None, self.in_shape,1))
 self.y = tf.placeholder(tf.float32, shape=(None, self.out_shape))

 self.layer0 = tf.layers.dense(self.x, 
 units=32, 
 activation=tf.nn.relu)
 self.layer1 = tf.layers.dense(self.layer0, 
 units=32, 
 activation=tf.nn.relu)

 # Create outputs and losses for all quantiles
 for i in range(self.num_quantiles):
 q = self.quantiles[i]

 # Get output layers 
 output = tf.layers.dense(self.layer1, 1, name="_q".format(i, int(q*100)))
 self.outputs.append(output)

 # Create losses

 error = tf.subtract(self.y, output)
 loss = tf.reduce_mean(tf.maximum(q*error, (q-1)*error), axis=-1)

 self.losses.append(loss)

 # Create combined loss
 self.combined_loss = tf.reduce_mean(tf.add_n(self.losses))
 self.train_step = tf.train.AdamOptimizer().minimize(self.combined_loss)
 print("Completed")

 def fit(self, x, y, epochs=100): 
 for epoch in range(epochs):
 epoch_losses = []
 for idx in range(0, x.shape[1], self.batch_size):
 batch_x = x[idx : min(idx + self.batch_size, x.shape[1]),:]
 batch_y = y[idx : min(idx + self.batch_size, y.shape[1]),:]

 feed_dict = self.x: batch_x,
 self.y: batch_y

 _, c_loss = self.sess.run([self.train_step, self.combined_loss], feed_dict)
 epoch_losses.append(c_loss)

 epoch_loss = np.mean(epoch_losses)
 self.loss_history.append(epoch_loss)
 if epoch % 100 == 0:
 print("Epoch : ".format(epoch, epoch_loss))
 print("Completed")

 def predict(self, x): 
 # Run model to get outputs
 feed_dict = self.x: x
 predictions = sess.run(self.outputs, feed_dict)

 return predictions

The model complies fine but I'm getting the following error when I try to fit the model

TypeError: '(slice(0, 32, None), slice(None, None, None))' is an invalid key

I'm unable to figure out where I'm missing. Appreciate any input.