2_11

2_11.py

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+import tensorflow as tf
+import numpy as np
+
+# prepare dataset
+def raw_data():
+    for i in range(2**4):
+        for j in range(2**4):
+            yield [[float(x) for x in '%04d%04d'%(int(bin(i)[2:]),int(bin(j)[2:]))], [float(x) for x in '%05d'%int(bin(i + j)[2:])]]
+raw = list(raw_data())
+rng = np.random.default_rng()
+rng.shuffle(raw)
+raw_len = len(raw)
+
+train_len = int(raw_len * 0.8)
+train_data, train_label = zip(*raw[:train_len])
+val_data, val_label = zip(*raw[train_len:])
+
+# learning params
+learning_rate = 0.1
+training_epochs = 200
+batch_size = 16
+
+# network params
+n_hidden_1 = 30
+n_hidden_2 = 11
+n_input = 8
+n_output = 5
+
+# model
+x = tf.placeholder("float", [None, n_input])
+y = tf.placeholder("float", [None, n_output])
+
+def mlp(_X, _weights, _biases):
+    layer1 = tf.nn.sigmoid(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1']))
+    layer2 = tf.nn.sigmoid(tf.add(tf.matmul(layer1, _weights['h2']), _biases['b2']))
+    return tf.matmul(layer2, _weights['out']) + _biases['out']
+
+weights = {
+    'h1' : tf.Variable(tf.random_normal([n_input, n_hidden_1])),
+    'h2' : tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
+    'out' : tf.Variable(tf.random_normal([n_hidden_2, n_output]))
+}
+
+biases = {
+    'b1' : tf.Variable(tf.random_normal([n_hidden_1])),
+    'b2' : tf.Variable(tf.random_normal([n_hidden_2])),
+    'out' : tf.Variable(tf.random_normal([n_output]))
+}
+
+pred = mlp(x, weights, biases)
+cost = tf.reduce_mean(tf.square(pred - y))
+optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
+
+init = tf.global_variables_initializer()
+
+train_data_batches = np.array_split(train_data, len(train_data) // batch_size)
+train_label_batches = np.array_split(train_label, len(train_label) // batch_size)
+
+correct_prediction = tf.equal(y, tf.where(pred<0.5, x = tf.zeros_like(pred), y = tf.ones_like(pred)))
+accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
+
+# training
+
+with tf.Session() as sess:
+    sess.run(init)
+    for epoch in range(training_epochs):
+        for i, j in zip(train_data_batches, train_label_batches):
+            sess.run(optimizer, feed_dict={x: i, y: j})
+        print("epoch:", epoch, "Train Accuracy:", accuracy.eval({x: train_data, y: train_label}), "Val Accuracy:", accuracy.eval({x: val_data, y: val_label}))