python - How to predict using trained Tensorflow model -

i have created , trained neural network able input test points , see results (rather using eval function).

the model runs fine , cost reduces every epoch, want add line @ end pass input coordinates , have tell me predicted transformed coordinates.

import tensorflow tf import numpy np  def coordinate_transform(size, angle):     input = np.random.rand(size, 2)     output = np.zeros((size, 2))     noise = 0.05*(np.add(np.random.rand(size) * 2, -1))     theta = np.add(np.add(np.arctan(input[:,1] / input[:,0]) , angle) , noise)     radii = np.sqrt(np.square(input[:,0]) + np.square(input[:,1]))     output[:,0] = np.multiply(radii, np.cos(theta))     output[:,1] = np.multiply(radii, np.sin(theta))     return input, output  #data input, output = coordinate_transform(2000, np.pi/2) train_in = input[:1000] train_out = output[:1000] test_in = input[1000:] test_out = output[1000:]  # parameters learning_rate = 0.001 training_epochs = 15 batch_size = 1 display_step = 1  # network parameters n_hidden_1 = 100 # 1st layer number of features n_input = 2 # [x,y] n_classes = 2 # output x,y coords  # tf graph input x = tf.placeholder("float", [1,n_input]) y = tf.placeholder("float", [1, n_input])  # create model def multilayer_perceptron(x, weights, biases):     # hidden layer relu activation     layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])     layer_1 = tf.nn.relu(layer_1)     # output layer linear activation     out_layer = tf.matmul(layer_1, weights['out']) + biases['out']     return out_layer  # store layers weight & bias weights = {     'h1': tf.variable(tf.random_normal([n_input, n_hidden_1])),     'out': tf.variable(tf.random_normal([n_hidden_1, n_classes])) } biases = {     'b1': tf.variable(tf.random_normal([n_hidden_1])),     'out': tf.variable(tf.random_normal([n_classes])) }  # construct model pred = multilayer_perceptron(x, weights, biases)  # define loss , optimizer #cost = tf.losses.mean_squared_error(0, (tf.slice(pred, 0, 1) - x)**2 + (tf.slice(pred, 1, 1) - y)**2) cost = tf.losses.mean_squared_error(y, pred) optimizer = tf.train.adamoptimizer(learning_rate=learning_rate) optimizer = optimizer.minimize(cost)  # initializing variables #init = tf.global_variables_initializer() init = tf.initialize_all_variables()  # launch graph tf.session() sess:     sess.run(init)      # training cycle     epoch in range(training_epochs):         avg_cost = 0.         total_batch = 1000#int(len(train_in)/batch_size)         # loop on batches         in range(total_batch):             batch_x = train_in[i].reshape((1,2))             batch_y = train_out[i].reshape((1,2))              #print(batch_x.shape)             #print(batch_y.shape)             #print(batch_y, batch_x)             # run optimization op (backprop) , cost op (to loss value)             _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,                                                           y: batch_y})             # compute average loss             avg_cost += c / total_batch         # display logs per epoch step         if epoch % display_step == 0:             print ("epoch:", '%04d' % (epoch+1), "cost=", \                 "{:.9f}".format(avg_cost))     print("optimization finished!")      # test model     correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))     # calculate accuracy     accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))      #make predictions 

well 'pred' op actual outcome (as it's used compare y when calculating loss), following should trick:

print(sess.run([pred], feed_dict={x: _input_goes_here_ }) 

obviously _input_goes_here_ need replaced actual input.