import tensorflow as tf import numpy as np x_data = np.array( [[0, 0], [1, 0], [1, 1], [0, 0], [0, 0], [0, 1]]) y_data = np.array([ [1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 0, 1] ]) global_step = tf.Variable(0, trainable=False, name='global_step') X = tf.placeholder(tf.float32, name='Input') Y = tf.placeholder(tf.float32, name='Output') with tf.name_scope('layer1'): W1 = tf.Variable(tf.random_uniform([2, 10], -1., 1.), name='W1') b1 = tf.Variable(tf.zeros([10]), name='b1') L1 = tf.add(tf.matmul(X, W1), b1, name='L1') L1 = tf.nn.relu(L1) with tf.name_scope('layer2'): W2 = tf.Variable(tf.random_uniform([10, 3], -1., 1.), name='W2') b2 = tf.Variable(tf.zeros([3]), name='b2') model = tf.add(tf.matmul(L1, W2), b2, name='model') prediction = tf.argmax(model, 1, name='prediction') cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=Y, logits=model), name='cost') optimizer = tf.train.AdamOptimizer(learning_rate=0.01, name='optimizer') train_op = optimizer.minimize(cost, global_step=global_step) init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) saver = tf.train.Saver(tf.global_variables()) merged = tf.summary.merge_all() writer = tf.summary.FileWriter('./logs', sess.graph) for step in range(30): sess.run(train_op, feed_dict={X: x_data, Y: y_data}) if (step + 1) % 30 == 0: print(step + 1, sess.run(cost, feed_dict={X: x_data, Y: y_data})) tf.train.write_graph(sess.graph_def, '.', './model/FFNN.pbtxt') saver.save(sess, './model/FFNN.ckpt', global_step=global_step) break target = tf.argmax(Y, 1) is_correct = tf.equal(prediction, target) accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32)) print('Accuracy: %.2f' % sess.run(accuracy * 100, feed_dict={X: x_data, Y: y_data}))

import numpy as np import tensorflow as tf x_data = np.array( [[0, 0], [1, 0], [1, 1], [0, 0], [0, 0], [0, 1]]) y_data = np.array([ [1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 0, 1] ]) model = tf.keras.models.Sequential() model.add(tf.keras.layers.Dense(10, input_dim=2,activation="relu")) model.add(tf.keras.layers.Dense(2, activation="relu", kernel_initializer="uniform")) model.add(tf.keras.layers.Dense(3)) model.add(tf.keras.layers.Activation("softmax")) adam = tf.keras.optimizers.Adam(lr=0.01) model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy']) model.summary() reult = model.fit(x_data, y_data, shuffle=True, epochs=10, batch_size=2, validation_data=(x_data, y_data))

from tensorflow.python.tools import freeze_graph freeze_graph.freeze_graph("model/FFNN.pbtxt", "", "", "model/FFNN.ckpt-90", "Output", "", "", "FFNN_frozen_graph.pb", True, "")

def load_graph(frozen_graph_filename): with tf.gfile.GFile(frozen_graph_filename, "rb") as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) with tf.Graph().as_default() as graph: tf.import_graph_def(graph_def, name="") return graph graph = load_graph('FFNN_frozen_graph.pb') for op in graph.get_operations(): print(op.name)

import tfcoreml mlmodel = tfcoreml.convert( tf_model_path = 'FFNN_frozen_graph.pb', mlmodel_path = 'FFNN.mlmodel', output_feature_names = ['layer2/prediction:0'], input_name_shape_dict = {'Input:0': [1, 2]})

import tensorflow as tf graph_def_file = "FFNN_frozen_graph.pb" input_arrays = ["Input"] output_arrays = ["layer2/prediction"] converter = tf.contrib.lite.TocoConverter.from_frozen_graph( graph_def_file, input_arrays, output_arrays) tflite_model = converter.convert() open("FFNN.tflite", "wb").write(tflite_model)

import coremltools coreml_model = coremltools.converters.keras.convert(model) coreml_model.save('FFNN.mlmodel')

keras_file = "FFNN.h5" tf.keras.models.save_model(model, keras_file) converter = tf.contrib.lite.TocoConverter.from_keras_model_file(keras_file) tflite_model = converter.convert() open("FFNN.tflite", "wb").write(tflite_model)