Tensorflow with batch size and wrong dimesion - tensorflow

Question

Here are my code:
# Model parameters: W and b
# tf.reset_default_graph()
W = tf.get_variable("weight",shape = [784, 10], dtype=tf.float32)
b = tf.get_variable("b", shape=(784,10), dtype= tf.float32)
input_X = tf.placeholder('float32', shape = (None,10))
input_y = tf.placeholder('float32', [784, 10])
ogits = W*input_X + b
probas = tf.nn.softmax(logits)
classes = tf.argmax(probas)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = input_y, logits = logits))
step = tf.train.AdamOptimizer(loss)
s.run(tf.global_variables_initializer())
BATCH_SIZE = 512
EPOCHS = 40
# for logging the progress right here in Jupyter (for those who don't have TensorBoard)
simpleTrainingCurves = matplotlib_utils.SimpleTrainingCurves("cross-entropy", "accuracy")
for epoch in range(EPOCHS): # we finish an epoch when we've looked at all training samples
batch_losses = []
for batch_start in range(0, X_train_flat.shape[0], BATCH_SIZE): # data is already shuffled
_, batch_loss = s.run([step, loss], {input_X: X_train_flat[batch_start:batch_start+BATCH_SIZE],
input_y: y_train_oh[batch_start:batch_start+BATCH_SIZE]})
# collect batch losses, this is almost free as we need a forward pass for backprop anyway
batch_losses.append(batch_loss)
train_loss = np.mean(batch_losses)
val_loss = s.run(loss, {input_X: X_val_flat, input_y: y_val_oh}) # this part is usually small
train_accuracy = accuracy_score(y_train, s.run(classes, {input_X: X_train_flat})) # this is slow and usually skipped
valid_accuracy = accuracy_score(y_val, s.run(classes, {input_X: X_val_flat}))
simpleTrainingCurves.add(train_loss, val_loss, train_accuracy, valid_accuracy)
the error is:
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py
in _run(self, handle, fetches, feed_dict, options, run_metadata)
973 'Cannot feed value of shape %r for Tensor %r, '
974 'which has shape %r'
--> 975 % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
976 if not self.graph.is_feedable(subfeed_t):
977 raise ValueError('Tensor %s may not be fed.' % subfeed_t)
ValueError: Cannot feed value of shape (512, 784) for Tensor
'Placeholder_2:0', which has shape '(?, 10)'
I am new at tensorflow and coursena is what I am learning. Please help me.

Answer 1

The issue is with the weights, Bias, input_x and input_y placeholder size.
Below is the modified code that should resolve your issue.
W = tf.get_variable('Weight', shape=(784,10), dtype=tf.float32)
b = tf.get_variable('bias', shape=(10,), dtype=tf.float32)
input_X = tf.placeholder(shape=(None, 784), dtype=tf.float32) #None is batch size and 784 is the input.
input_y = tf.placeholder(shape=(None, 10), dtype=tf.float32) # None is batch size, 10 is number of classess.
logits = tf.matmul(input_X, W) + b
probas = tf.nn.softmax(logits)
classes = tf.argmax(probas, 1)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=input_y))
step = tf.train.RMSPropOptimizer(0.001).minimize(loss)
s.run(tf.global_variables_initializer())
BATCH_SIZE = 512
EPOCHS = 50
simpleTrainingCurves = matplotlib_utils.SimpleTrainingCurves("cross-entropy", "accuracy")
for epoch in range(EPOCHS):
batch_losses = []
for batch_start in range(0, X_train_flat.shape[0], BATCH_SIZE): # data is already shuffled
_, batch_loss = s.run([step, loss], {input_X: X_train_flat[batch_start:batch_start+BATCH_SIZE],
input_y: y_train_oh[batch_start:batch_start+BATCH_SIZE]})
batch_losses.append(batch_loss)
train_loss = np.mean(batch_losses)
val_loss = s.run(loss, {input_X: X_val_flat, input_y: y_val_oh})
train_accuracy = accuracy_score(y_train, s.run(classes, {input_X: X_train_flat}))
valid_accuracy = accuracy_score(y_val, s.run(classes, {input_X: X_val_flat}))
simpleTrainingCurves.add(train_loss, val_loss, train_accuracy, valid_accuracy)