Tensorflow and Sklearn Is there a way to feed tensorflow tensors into a sklearn model? I have the following model to set up data compression:

with tf.name_scope('model'):   with tf.name_scope('setup'):
    token_size = X_train[0][0].shape[1]
    batch_size = 64
    dropout_1 = tf.placeholder_with_default(1. , shape=(), name='dropout_1')
    dropout_2 = tf.placeholder_with_default(1. , shape=(), name='dropout_2')
    # [None, 5, 19, 400]
    inputs = tf.placeholder(tf.float32, shape=(None, FLAGS.eval_beam_size, largest, token_size), name='inputs')
    label = tf.placeholder(tf.float32, shape=(None, FLAGS.eval_beam_size), name='label')

  with tf.name_scope('layer1'):
    hid_size_1 = 20
    # [400, 5]
    w1 = tf.Variable(tf.random_normal([token_size ,hid_size_1], stddev=0.01), name='w1')
    b1 = tf.Variable(tf.constant(0.1, shape=([hid_size_1])), name='b1')
    y1 = tf.nn.dropout(tf.nn.tanh(tf.add(tf.tensordot(inputs, w1, 1), b1)), dropout_1, name='dropout_1_op')

    tf.summary.histogram('layer1_weights', w1)
    tf.summary.histogram('layer1_bias', b1)

  with tf.name_scope('layer_2'):
    hid_size_2 = 5   # [400, 5]
    w2 = tf.Variable(tf.random_normal([hid_size_1, hid_size_2], stddev=0.01), name='w2')
    b2 = tf.Variable(tf.constant(0.1, shape=([hid_size_2])), name='b2')
    y2 = tf.nn.dropout(tf.nn.tanh(tf.add(tf.tensordot(y1, w2, 1), b2)), dropout_2, name='dropout_2_op')

    tf.summary.histogram('layer2_weights', w2)
    tf.summary.histogram('layer2_bias', b2)

  with tf.name_scope('layer_3'):
    hid_size_3 = 1
    w3 = tf.Variable(tf.random_normal([hid_size_2, hid_size_3], stddev=0.01), name='w3')
    b3 = tf.Variable(tf.constant(0.1, shape=([hid_size_3])), name='b3')
    y3 = tf.nn.dropout(tf.nn.tanh(tf.add(tf.tensordot(y2, w3, 1), b3)), dropout_2, name='dropout_3_op')

    tf.summary.histogram('layer3_weights', w3)
    tf.summary.histogram('layer3_bias', b3)

with tf.name_scope('support_ops'):   summed = tf.squeeze(tf.reduce_sum(y3, axis=2), axis=-1, name='summed')


From here I would like to be able to feed the outputs into a sklearn model and perhaps leave these layers to train based on the model output. Is this possible?
Yes it is possible. Once you actually return the results from the Tensorflow model, they will (by default) be returned as NumPy arrays. You can then use them as input e.g. to a SciKit Learn model.

Have a look at this thread, which shows some nice examples the types returned by TF models.

One last consideration: The model must return results in such a way that they are in the normal memory, reachable for the CPU - not somehow left in GPU memory.