issue with builiding of nueral network on the current version of tensorflow

[Shahbazmirza1]

Graph input/output

x = tf.placeholder(tf.float32, [None, n_steps, n_input]) y = tf.placeholder(tf.float32, [None, n_classes])

Graph weights

weights = { 'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights 'out': tf.Variable(tf.random_normal([n_hidden, n_classes], mean=1.0)) } biases = { 'hidden': tf.Variable(tf.random_normal([n_hidden])), 'out': tf.Variable(tf.random_normal([n_classes])) }

pred = LSTM_RNN(x, weights, biases)

Loss, optimizer and evaluation

l2 = lambda_loss_amount * sum( tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables() ) # L2 loss prevents this overkill neural network to overfit the data cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=pred)) + l2 # Softmax loss optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer

correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

for this part of the code

[xaxfan]

来信已收到，谢谢！

[Shahbazmirza1]

i want it working on urgent basis