Could someone help or guide me through what I should do better in order for this to work?
I changed the number of inputs to 2 and generated some random data, "x1" and "x2" (one number to be added to another). The idea is to use variables "add" and "mul" as the real output and base the cost (variable "Y") off of that, but I'm having trouble manipulating the data so it inputs properly.
I tried to make another variable with
x = tf.Variable([100 * np.random.random_sample([100]), 100 * np.random.random_sample([100]))
and a few other alteative ways, but that caused errors. Also, if there's anything else wrong in my code, please critique it! Anything helps.
Thank you.
'''
A Recurrent Neural Network implementation example using TensorFlow Library.
Author: *********
'''
import numpy as np
import tensorflow as tf
from tensorflow.models.n import n, n_cell
# import matplotlib.pyplot as plt
# from mpl_toolkits.mplot3d import Axes3D
# Parameters
training_iters = 1000
n_epochs = 1000
batch_size = 128
display_step = 100
leaing_rate = 0.001
n_observations = 100
n_input = 2 # Input data (Num + Num)
n_steps = 28 # timesteps
n_hidden_1 = 256 # 1st layer number of features
n_hidden_2 = 256 # 2nd layer number of features
n_classes = 1 # Output
X = tf.placeholder("float", [None, n_input])
X1 = tf.placeholder(tf.float32)
X2 = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)
# Random input data
x1 = 100 * np.random.random_sample([100,])
x2 = 100 * np.random.random_sample([100,])
add = tf.add(x1, x2)
mul = tf.mul(X1, X2)
weights = { 'hidden1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), #'hidden2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), 'out': tf.Variable(tf.random_normal([n_hidden_1, n_classes]))
}
biases = { 'hidden1': tf.Variable(tf.random_normal([n_hidden_1])), #'hidden2': tf.Variable(tf.random_normal([n_hidden_2])), 'out': tf.Variable(tf.random_normal([n_classes]))
}
def RNN(_X1, _weights, _biases): # Layer 1.1 layer_1 = tf.add(tf.matmul(_X1, weights['hidden1']), biases['hidden1']) layer_1 = tf.nn.relu(layer_1) # Layer 1.2 # layer_1_2 = tf.add(tf.matmul(_X2, weights['hidden2']), biases['hidden2']) # layer_1_2 = tf.nn.relu(layer_1_2) # Hidden layer with RELU activation layer_2 = tf.add(tf.matmul(layer_1, weights['out']), biases['out']) output = tf.nn.relu(layer_2) retu output
pred = RNN(X1, weights, biases)
cost = tf.reduce_sum(tf.pow(pred - Y, 2)) / (n_observations - 1)
optimizer = tf.train.AdamOptimizer(leaing_rate=leaing_rate).minimize(cost) # Adam Optimizer
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(Y,1))
init = tf.initialize_all_variables()
# initData = tf.initialize_variables(x1.all(), x2.all())
with tf.Session() as sess: # Here we tell tensorflow that we want to initialize all # the variables in the graph so we can use them sess.run(init) # Fit all training data prev_training_cost = 0.0 for epoch_i in range(n_epochs) : for (_x1) in x1: for (_x2) in x2: print("Input 1:") print(_x1) print("Input 2:") print(_x2) print("Add function: ") print(sess.run(add, feed_dict={X1: x1, X2: x2})) y = sess.run(add, feed_dict={X1: x1, X2: x2}) print(y) sess.run(optimizer, feed_dict={X: x, Y: y}) training_cost = sess.run( cost, feed_dict={X: xs, Y: ys}) print(training_cost) if epoch_i % 20 == 0: ax.plot(X1, X2, pred.eval( feed_dict={X1: x1, X2: x2}, session=sess), 'k', alpha=epoch_i / n_epochs) fig.show() plt.draw() # Allow the training to quit if we've reached a minimum if np.abs(prev_training_cost - training_cost) < 0.000001: break prev_training_cost = training_cost