Getting predictions in Logistic Regression (tf)

Question

I am new to Tensorflow and I still have troubles understanding how it works. I saw some examples but I am still not sure. I am trying to print the predictions and the accuracy.

I have this code:

def linear_function(x, w, b):
y_est = tf.add(tf.matmul(w, x), b)
y_est = tf.reshape(y_est, [])
return y_est


def initialize_parameters():
    W = tf.get_variable('W', [1, num_of_features], 
initializer=tf.contrib.layers.xavier_initializer())
    b = tf.get_variable("b1", [1, 1], initializer=tf.zeros_initializer())
    return W, b


if __name__ == '__main__':
    trainSetX, trainSetY = utils.load_train_set(num_of_examples)

    # create placeholders & variables
    X = tf.placeholder(tf.float32, shape=(num_of_features,))
    X_reshaped = tf.reshape(X, [num_of_features, 1])
    y = tf.placeholder(tf.float32, shape=())
    W, b = initialize_parameters()

    # prediction
    y_estim = linear_function(X_reshaped, W, b)
    y_pred = tf.sigmoid(y_estim)

    # set the optimizer
    loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=y_pred)
    loss_mean = tf.reduce_mean(loss)
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=alpha).minimize(loss_mean)

    # training phase
    init = tf.global_variables_initializer()
    with tf.Session() as sess:
        sess.run(init)
        for idx in range(num_of_examples):
            cur_x, cur_y = trainSetX[idx], trainSetY[idx]
            _, c = sess.run([optimizer, loss_mean], feed_dict={X: cur_x, y: cur_y})

So, now I want to actually read the values of y_pred and calculate the accuracy.

In some other sources I saw people adding this line to with tf.Session() as sess:

correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval(feed_dict={X: trainSetX.T, y: trainSetY}))

Clearly, it does not work for me, because my trainSetX has all the examples, while X is a placeholder for only 1 example at a time. I have tried to put the correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) and modify another like like this:

for idx in range(num_of_examples):
    cur_x, cur_y = trainSetX[idx], trainSetY[idx]
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    _, c, acc = sess.run([optimizer, loss_mean, correct_prediction], feed_dict={X: cur_x, y: cur_y})

But it just gives the following arror for ArgMax (Why?)

InvalidArgumentError (see above for traceback): Expected dimension in the range [0, 0), but got 1 [[Node: ArgMax_1 = ArgMax[T=DT_FLOAT, Tidx=DT_INT32, output_type=DT_INT64, _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_Placeholder_1_0_1, ArgMax/dimension)]]

Getting predictions in Logistic Regression (tf)

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