Python \ Tensorflow - Input to reshape is a tensor with 876500 values, but the requested shape requires a multiple of 3075

I am using the following code to train on my data but I am getting the error that 'Input to reshape is a tensor with 876500 values'. Now this number 876500 seems to be coming from the line in the very end of the loop which is Batch4 = np.reshape(batch1, [50,274 * 64]) in this line 50*17536 = 876500 but what I dont understand is why this throwing an error because for my placeholder 1 I have used the same dimensions as for Batch 4.

Training Matrix Size--> 9816 x 274 x 64

x = tf.placeholder(tf.float32, shape=[None, 274 * 64])
y_ = tf.placeholder(tf.float32, shape=[None, 2])

W = tf.Variable(tf.zeros([17536, 2]))
b = tf.Variable(tf.zeros([2]))
sess = tf.InteractiveSession()

sess.run(tf.global_variables_initializer())
y = tf.matmul(x,W) + b

def next_batch(num, data, labels):
'''
Return a total of `num` random samples and labels.
'''
ix = np.arange(0 , len(data))
np.random.shuffle(idx)
idx = idx[:num]
data_shuffle = [data[ i] for i in idx]
labels_shuffle = [labels[ i] for i in idx]
return np.asarray(data_shuffle), np.asarray(labels_shuffle)
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)

W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

x_image = tf.reshape(x, [x.shape[0], 274, 64, 1])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

W_fc1 = weight_variable([69 *16 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 69*16*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 2])
b_fc2 = bias_variable([2])

y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  for i in range(1000):
      batch1,batch2 = next_batch(50,Training, Class_Training)
      Batch4 = np.reshape(batch1, [50,274 * 64])
      if i % 100 == 0:
          train_accuracy = accuracy.eval(feed_dict={
          x: Batch4, y_: batch2, keep_prob: 1.0})
          print('step %d, training accuracy %g' % (i, train_accuracy))
          train_step.run(feed_dict={x: Batch4, y_: batch2, keep_prob: 0.5})