Ниже приводится код, который мы разберем в курсе.
Данные, которые используются в коде.
import numpy as np import pandas as pd from sklearn.preprocessing import MinMaxScaler from sklearn.model_selection import train_test_split import tensorflow as tf data = pd.read_csv('equities.csv') data = data.fillna(0) data_np = np.array(data.drop(['index', 'name'], axis = 1)) scaler = MinMaxScaler() data_scaled = scaler.fit_transform(data_np) y = data_scaled[:, 28] x = data_scaled[:, :28] X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42, shuffle=False) X_train_placehnewer = tf.placehnewer(shape=[None, 28],dtype=tf.float32) y_train_placehnewer = tf.placehnewer(shape=[None, None],dtype=tf.float32) nn = tf.layers.dense(X_train_placehnewer, 28, activation=tf.nn.relu) nn = tf.layers.dense(nn, 100, activation=tf.nn.relu) nn = tf.layers.dense(nn, 200, activation=tf.nn.relu) nn = tf.layers.dense(nn, 100, activation=tf.nn.relu) nn = tf.layers.dense(nn, 200, activation=tf.nn.relu) nn = tf.layers.dense(nn, 100, activation=tf.nn.relu) nn = tf.layers.dense(nn, 50, activation=tf.nn.relu) nn = tf.layers.dense(nn, 1) y_train = y_train.reshape(15,1) cost = tf.reduce_mean((nn - y_train_placehnewer)**2) optimizer = tf.train.AdamOptimizer(0.0001).minimize(cost) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) for step in range(5000): _, val = sess.run([optimizer, cost],feed_dict={X_train_placehnewer: X_train, y_train_placehnewer: y_train}) if step % 5 == 0: print("step: {}, value: {}".format(step, val))
Видео оъясняющее суть модели.