ValueError when combining Tensorflow with Sklearn's Sparse Matrices

Question

Hello and let me start with by saying my experience with StackOverflow is extremely limited likewise with my experience programming. Needless to say, if I make any unintentional mistakes in the format of StackOverflow or the format of my code, I apologize and am welcome to edit my post.

I've done extensive research so to not create a duplicate and found posts like this one but no matter how hard I try, I either cant derive meaning from the post, or have too little knowledge in how to implement it

So here's my problem:

I'm making similar to a sentiment analysis machine learning algorithm, and I use Sklearns CountVectorizer and Tfidf on about 6,000 tweets, which turned into the following <6612x9703 sparse matrix of type '<class 'numpy.float64'> with 47719 stored elements in Compressed Sparse Row format>

From there I try to train on it using the following function:

X = tf.placeholder("float", [None, X_train_spmx.shape[0]])
y = tf.placeholder("float")


def train_neural_net(x):
prediction = neural_net_model(x)

cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits_v2(logits=prediction, labels=y) )
optimizer = tf.train.AdamOptimizer().minimize(cost)

hm_epochs = 10
with tf.Session() as sess:

    sess.run(tf.global_variables_initializer())

    for epoch in range(hm_epochs):
        epoch_loss = 0

        i = 0
        while i < len(y_train):
            start = i
            end = i+batch_size

            batch_x = np.array(X_train_spmx[start:end])
            print(batch_x)
            batch_y = np.array(y_train[start:end])
            print(batch_y)

            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
            epoch_loss += c

        print('Epoch', epoch, 'completed out of',hm_epochs,'loss:',epoch_loss)

    correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
    print('Accuracy:',accuracy.eval({x:X_test, y:y_test}))

train_neural_net(X)

However when I run I get the error:

    ValueError                                Traceback (most recent call 
    last)
    <ipython-input-16-8130c429fbca> in <module>()
         32         print('Accuracy:',accuracy.eval({x:X_test, y:y_test}))
         33 
    ---> 34 train_neural_net(X)

    <ipython-input-16-8130c429fbca> in train_neural_net(x)
         23                 print(batch_y)
         24 
    ---> 25                 _, c = sess.run([optimizer, cost], feed_dict= . 
  {x: batch_x, y: batch_y})
         26                 epoch_loss += c
         27 

    /anaconda3/lib/python3.6/site- 
   packages/tensorflow/python/client/session.py in run(self, fetches, 
    feed_dict, options, run_metadata)
        903     try:
        904       result = self._run(None, fetches, feed_dict, options_ptr,
    --> 905                          run_metadata_ptr)
        906       if run_metadata:
        907         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

    /anaconda3/lib/python3.6/site- 
   packages/tensorflow/python/client/session.py in _run(self, handle, 
    fetches, feed_dict, options, run_metadata)
       1104             feed_handles[subfeed_t] = subfeed_val
       1105           else:
    -> 1106             np_val = np.asarray(subfeed_val, 
    dtype=subfeed_dtype)
       1107 
       1108           if (not is_tensor_handle_feed and

    /anaconda3/lib/python3.6/site-packages/numpy/core/numeric.py in 
    asarray(a, dtype, order)
        490 
        491     """
    --> 492     return array(a, dtype, copy=False, order=order)
        493 
        494 

    ValueError: setting an array element with a sequence.

Edit:

After a recommendation that I turn the Spare Matrix into an array using .toarray() , I receive the error ValueError: Cannot feed value of shape (128, 9703) for Tensor 'Placeholder_2:0', which has shape '(?, 6612)' I researched and believe it is not the same as here as I have different values in the PlaceHolder, meanwhile the link had different Dimensions. Still unsure on where to go, thanks!

Any Ideas? Thanks in advance