How to print out prediction value in tensorflow - tensorflow

Question

I am new to tensorflow and I am a slow learner. After successfully compiling the model and get the accuracy I want to print the prediction variable but I dont know how to do it.
My dataset is multivariate feature with only one output. The output contains only 1, 0 ,-1 so I made one hot encoder for the output. I finished compiling the model and looking for computing prediction on tensorflow online, however I didnt find a good solution base on my question.
The precisionCalculate function is to compute precision on each column on test data since the trian_y and test_y after one hot encode becomes [1,0,0],[0,1,0],[0,0,1].
I have tried
y_pred = sess.run(tf.argmax(y, 1), feed_dict={X: test_x, y: test_y})
but it turns out y_pred is exactly the same as my test_y
Here is my full code example.
import tensorflow as tf
import pandas as pd
import numpy as np
import tensorflow.contrib.rnn
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, LabelEncoder
import pdb
np.set_printoptions(threshold=np.inf)
def precisionCalculate(pred_y, test_y):
count = pred_y + test_y
firstZero = len(count[count==0])
countFour = len(count[count == 4])
precision1 = firstZero / len(pred_y[pred_y==0] )
precision3 = countFour / len(pred_y[pred_y==2])
pdb.set_trace()
return precision1, precision3
df = pd.read_csv('new_df.csv', skiprows=[0], header=None)
df.drop(columns=[0,1], inplace=True)
df.columns = [np.arange(0, df.shape[1])]
df[0] = df[0].shift(-1)
#parameters
time_steps = 1
inputs = df.shape[1]
outputs = 3
#remove nan as a result of shift values
df = df.iloc[:-1, :]
#convert to numpy
df = df.values
train_number = 30276 #start date from 1018
train_x = df[: train_number, 1:]
test_x = df[train_number:, 1:]
train_y = df[:train_number, 0]
test_y = df[train_number:, 0]
#data pre-processing
#x y split
#scale
scaler = MinMaxScaler(feature_range=(0,1))
train_x = scaler.fit_transform(train_x)
test_x = scaler.fit_transform(test_x)
#reshape into 3d array
train_x = train_x[:, None, :]
test_x = test_x[:, None, :]
#one-hot encode the outputs
onehot_encoder = OneHotEncoder()
#encoder = LabelEncoder()
max_ = train_y.max()
max2 = test_y.max()
train_y = (train_y - max_) * (-1)
test_y = (test_y - max2) * (-1)
encode_categorical = train_y.reshape(len(train_y), 1)
encode_categorical2 = test_y.reshape(len(test_y), 1)
train_y = onehot_encoder.fit_transform(encode_categorical).toarray()
test_y = onehot_encoder.fit_transform(encode_categorical2).toarray()
print(train_x.shape, train_y.shape, test_x.shape, test_y.shape)
#model parameters
learning_rate = 0.001
epochs = 100
batch_size = int(train_x.shape[0]/10)
length = train_x.shape[0]
display = 100
neurons = 100
tf.reset_default_graph()
X = tf.placeholder(tf.float32, [None, time_steps, 90],name='x')
y = tf.placeholder(tf.float32, [None, outputs],name='y')
#LSTM cell
cell = tf.contrib.rnn.BasicLSTMCell(num_units = neurons, activation = tf.nn.relu)
cell_outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
# pass into Dense layer
stacked_outputs = tf.reshape(cell_outputs, [-1, neurons])
out = tf.layers.dense(inputs=stacked_outputs, units=outputs)
# squared error loss or cost function for linear regression
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=out, labels=y))
# optimizer to minimize cost
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
training_op = optimizer.minimize(loss)
accuracy = tf.metrics.accuracy(labels = tf.argmax(y, 1), predictions = tf.argmax(out, 1), name = "accuracy")
precision = tf.metrics.precision(labels=tf.argmax(y, 1), predictions=tf.argmax(out, 1), name="precision")
recall = tf.metrics.recall(labels=tf.argmax(y, 1), predictions=tf.argmax(out, 1),name="recall")
f1 = 2 * accuracy[1] * recall[1] / ( precision[1] + recall[1] )
with tf.Session() as sess:
# initialize all variables
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
# Train the model
for steps in range(epochs):
mini_batch = zip(range(0, length, batch_size), range(batch_size, length+1, batch_size))
epoch_loss = 0
i = 0
# train data in mini-batches
for (start, end) in mini_batch:
sess.run(training_op, feed_dict = {X: train_x[start:end,:,:], y: train_y[start:end,:]})
# print training performance
if (steps+1) % display == 0:
# evaluate loss function on training set
loss_fn = loss.eval(feed_dict = {X: train_x, y: train_y})
print('Step: {} \tTraining loss: {}'.format((steps+1), loss_fn))
# evaluate model accuracy
acc, prec, recall, f1 = sess.run([accuracy, precision, recall, f1],feed_dict = {X: test_x, y: test_y})
y_pred = sess.run(tf.argmax(y, 1), feed_dict={X: train_x, y: train_y})
test_y_alter = np.argmax(test_y, axis=1)
#print(test_y_alter)
print(precisionCalculate(y_pred, test_y_alter))
print(y_pred)
#prediction = y_pred.eval(feed_dict={X: train_x, y: test_y})
#print(prediction)
print('\nEvaluation on test set')
print('Accuracy:', acc[1])
print('Precision:', prec[1])
print('Recall:', recall[1])
print('F1 score:', f1)

Answer 1

I think you should use the output of your model instead of the label (y) in tf.argmax.
Here is my code in order to print prediction of the model:
pred_y = tf.Print(tf.argmax(score, 1), [tf.argmax(score, 1)], message="prediction:)
pred_y.eval()
In the above code, score means the probability output of your model.