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)