Question

我想将kotlin代码作为脚本运行来自java Java Scripting API，类似于javascript：

import javax.script.*;
public class EvalScript {
    public static void main(String[] args) throws Exception {
        // create a script engine manager
        ScriptEngineManager factory = new ScriptEngineManager();
        // create a JavaScript engine
        ScriptEngine engine = factory.getEngineByName("JavaScript");
        // evaluate JavaScript code from String
        engine.eval("print('Hello, World')");
    }
}

或使用类似的API。

Answer 1

是的，可以从Kotlin 1.1开始：http://kotlinlang.org/docs/reference/whatsnew11.html#javaxscript-support

此配置将Kotlin脚本引擎添加到我的Kotlin 1.2项目中：

<dependency> <groupId>org.jetbrains.kotlin</groupId> <artifactId>kotlin-script-runtime</artifactId> <version>${kotlin.version}</version> </dependency> <dependency> <groupId>org.jetbrains.kotlin</groupId> <artifactId>kotlin-script-util</artifactId> <version>${kotlin.version}</version> </dependency>文件，内容来自https://github.com/JetBrains/kotlin/blob/master/libraries/examples/kotlin-jsr223-local-example/src/main/resources/META-INF/services/javax.script.ScriptEngineFactory
2个图书馆： kotlin-script-util

<强>更新从Kotlin 1.2.20 kotlin-compiler开始并不明确依赖<dependency> <groupId>org.jetbrains.kotlin</groupId> <artifactId>kotlin-compiler-embeddable</artifactId> <version>${kotlin.version}</version> </dependency>（参见https://youtrack.jetbrains.com/issue/KT-17561）。因此应该提供另外一个模块（截至build file in example project）： import tensorflow as tf tf.set_random_seed(1337) import numpy as np np.random.seed(1337) import random random.seed(1337) # Train-test split data_train, data_test, labels_train, labels_test = train_test_split(data, labels_, test_size=TEST_SIZE, random_state=RANDOM_STATE) def DeepMLPClassifier(_X, _weights, _biases, dropout_keep_prob): layer1 = tf.nn.dropout(tf.nn.tanh(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])), dropout_keep_prob) layer2 = tf.nn.dropout(tf.nn.tanh(tf.add(tf.matmul(layer1, _weights['h2']), _biases['b2'])), dropout_keep_prob) layer3 = tf.nn.dropout(tf.nn.tanh(tf.add(tf.matmul(layer2, _weights['h3']), _biases['b3'])), dropout_keep_prob) layer4 = tf.nn.dropout(tf.nn.tanh(tf.add(tf.matmul(layer3, _weights['h4']), _biases['b4'])), dropout_keep_prob) out = ACTIVATION_FUNCTION_OUT(tf.add(tf.matmul(layer4, _weights['out']), _biases['out'])) return out # Here are the dictionary of weights and biases of each layer weights = { 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1], stddev=STDDEV, seed=1337)), 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], stddev=STDDEV, seed=1337)), 'h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3], stddev=STDDEV, seed=1337)), 'h4': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_4], stddev=STDDEV, seed=1337)), 'out': tf.Variable(tf.random_normal([n_hidden_4, n_classes], stddev=STDDEV, seed=1337)), } biases = { 'b1': tf.Variable(tf.random_normal([n_hidden_1], seed=1337)), 'b2': tf.Variable(tf.random_normal([n_hidden_2], seed=1337)), 'b3': tf.Variable(tf.random_normal([n_hidden_3], seed=1337)), 'b4': tf.Variable(tf.random_normal([n_hidden_4], seed=1337)), 'out': tf.Variable(tf.random_normal([n_classes], seed=1337)) } # Build model pred = DeepMLPClassifier(X, weights, biases, dropout_keep_prob) # Loss and optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) # softmax loss optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE).minimize(cost) # Accuracy correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print("Deep MLP networks has been built successfully...") print("Starting training...") # ------------------------------------------------------------------------------ # Initialize variables init_op = tf.global_variables_initializer() # Launch session sess = tf.Session() sess.run(init_op) # tf.set_random_seed(1) acc_list = [] cost_list = [] i_data = [] # Training loop for epoch in range(TRAINING_EPOCHS): avg_cost = 0.0 total_batch = int(data_train.shape[0] / BATCH_SIZE) # Loop over all batches for i in range(total_batch): randidx = np.random.randint(int(TRAIN_SIZE), size=BATCH_SIZE) batch_xs = data_train[randidx, :] batch_ys = labels_train[randidx, :] # Fit using batched data sess.run(optimizer, feed_dict={X: batch_xs, y: batch_ys, dropout_keep_prob: 0.9}) # Calculate average cost avg_cost += sess.run(cost, feed_dict={X: batch_xs, y: batch_ys, dropout_keep_prob: 1.}) / total_batch # Display progress # if epoch % DISPLAY_STEP == 0: i_data.append(epoch + 1) cost_list.append(avg_cost) print("\n Epoch:%3d/%3d, cost:%.9f" % (epoch, TRAINING_EPOCHS, avg_cost)) train_acc = sess.run(accuracy, feed_dict={X: batch_xs, y: batch_ys, dropout_keep_prob: 1.}) acc_list.append(train_acc) print("Training accuracy: %.3f" % (train_acc))

Answer 2

Kotlin对Java Scripting API的支持是planned，但是从版本1.0.3开始还没有。同时，您可以尝试使用现有的open-source implementation。

我可以使用Java Scripting API将kotlin作为脚本运行

2 个答案: