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- iris = datasets.load_iris()
- x_vals = np.array([x[0:3] for x in iris.data])
- y_vals = np.array([x[3] for x in iris.data])
- sess = tf.Session()
- seed = 2
- tf.set_random_seed(seed)
- np.random.seed(seed)
- train_indices = np.random.choice(len(x_vals), round(len(x_vals)*0.8),
- replace=False)
- test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices)))
- x_vals_train = x_vals[train_indices]
- x_vals_test = x_vals[test_indices]
- y_vals_train = y_vals[train_indices]
- y_vals_test = y_vals[test_indices]
- def normalize_cols(m):
- col_max = m.max(axis=0)
- col_min = m.min(axis=0)
- return (m-col_min) / (col_max - col_min)
- x_vals_train = np.nan_to_num(normalize_cols(x_vals_train))
- x_vals_test = np.nan_to_num(normalize_cols(x_vals_test))
- batch_size = 50
- x_data = tf.placeholder(shape=[None, 3], dtype=tf.float32)
- y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)
- hidden_layer_nodes = 5
- A1 = tf.Variable(tf.random_normal(shape=[3,hidden_layer_nodes]))
- b1 = tf.Variable(tf.random_normal(shape=[hidden_layer_nodes]))
- A2 = tf.Variable(tf.random_normal(shape=[hidden_layer_nodes,1]))
- b2 = tf.Variable(tf.random_normal(shape=[1]))
- hidden_output = tf.nn.relu(tf.add(tf.matmul(x_data, A1), b1))
- final_output = tf.nn.relu(tf.add(tf.matmul(hidden_output, A2),b2))
- loss = tf.reduce_mean(tf.square(y_target - final_output))
- my_opt = tf.train.GradientDescentOptimizer(0.005)
- train_step = my_opt.minimize(loss)
- init = tf.global_variables_initializer()
- sess.run(init)
- # First we initialize the loss vectors for storage.
- loss_vec = []
- test_loss = []
- for i in range(500):
- # First we select a random set of indices for the batch.
- rand_index = np.random.choice(len(x_vals_train), size=batch_size)
- # We then select the training values
- rand_x = x_vals_train[rand_index]
- rand_y = np.transpose([y_vals_train[rand_index]])
- # Now we run the training step
- sess.run(train_step, feed_dict={x_data: rand_x, y_target:rand_y})
- # We save the training loss
- temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})
- loss_vec.append(np.sqrt(temp_loss))
- # Finally, we run the test-set loss and save it.
- test_temp_loss = sess.run(loss, feed_dict={x_data: x_vals_test,
- y_target: np.transpose([y_vals_test])})
- test_loss.append(np.sqrt(test_temp_loss))
- if (i+1)%50==0:
- print('Generation: ' + str(i+1) + '. Loss = ' + str(temp_loss))
- plt.plot(loss_vec, 'k-', label='Train Loss')
- plt.plot(test_loss, 'r--', label='Test Loss')
- plt.title('Loss (MSE) per Generation')
- plt.xlabel('Generation')
- plt.ylabel('Loss')
- plt.legend(loc='upper right')
- plt.show()
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