Reinforcement learning (RL) is a framework where agents learn to perform actions in an environment so as to maximize a reward. It’s actually training an AI to learn through every mistake and find the correct path without any label. The two main components are the environment and the agent. Deep Reinforcement learning (DRL) combined with deep learning technology is even more powerful. AlphaGo, is a typical application of deep reinforcement learning. ...
Tensorflow2.X和1.X有多了很多差別和使用方式， 今天用tf2來實作MNIST分類問題 MNIST MNIST是一個很標準的手寫數字分類問題， 數據集下載有很多方式，這次直接使用tf API提供的 28 * 28 且只有黑白的數據 開發 在local 起 jupyter lab 先看看GPU是否啟用 %matplotlib widget import matplotlib.pyplot as plt import tensorflow as tf import numpy as np # check gpu tf.config.list_physical_devices('GPU') tf.test.is_built_with_cuda() # output True 方法一 繼承 tf.keras.model class MLP(tf.keras.Model): def __init__(self): super().__init__() self.flatten = tf.keras.layers.Flatten() self.dense1 = tf.keras.layers.Dense(units=100, activation=tf.nn.relu) self.dense2 = tf.keras.layers.Dense(units=20, activation=tf.nn.leaky_relu) self.dense3 = tf.keras.layers.Dense(units=10) @tf.function def call(self, inputs): # [batch_size, 28, 28, 1] flat1 = self.flatten(inputs) # [batch_size, 784] dens1 = self.dense1(flat1) # [batch_size, 100] dens2 = self.dense2(dens1) # [batch_size, 20] dens3 = self.dense3(dens2) # [batch_size, 10] output = tf.nn.softmax(dens3) return output 使用tf.GradientTape訓練 # @tf.function def one_batch_step(X, y, **kwargs): with tf.GradientTape() as tape: y_pred = model(X) loss = tf.keras.losses.sparse_categorical_crossentropy(y_true=y, y_pred=y_pred) loss = tf.reduce_mean(loss) tf.print(f"{batch_index} loss {loss}", [loss]) with summary_writer.as_default(): tf.summary.scalar("loss", loss, step=batch_index) grads = tape.gradient(loss, model.variables) optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables)) for epoch_index in range(num_epochs): for batch_index in range(num_batches): X, y = data_loader.get_batch(batch_size) one_batch_step(X, y, batch_index=batch_index) with summary_writer.as_default(): tf.summary.trace_export(name="model_trace", step=0, profiler_outdir=log_dir) tf.saved_model.save(model, f"saved/{model_name}") 方法二 使用keras Pipeline來疊每一層要用的函數，彈性較低，但非常適合簡單的Model ...
這是進入 Kaggle 的第一個試題：Kaggle digit recognizer。 這是一個用 CSV 儲存的 MNIST 問題，因此選用 CNN 來解決。資料格式如下： If we omit the “pixel” prefix, the pixels make up the image like this: 000 001 002 003 ... 026 027 028 029 030 031 ... 054 055 056 057 058 059 ... 082 083 | | | | ... | | 728 729 730 731 ... 754 755 756 757 758 759 ... 782 783 The test data set, (test.csv), is the same as the training set, except that it does not contain the “label” column. Your submission file should be in the following format: ...