민식이 과제

keras_homework.py

from keras.models import Sequential
from keras.layers import Dense
from sklearn.model_selection import train_test_split

import pandas as pd
import numpy as np
import tensorflow as tf

seed=0
np.random.seed(seed)
tf.set_random_seed(seed)
df=pd.read_csv("./dataset.csv")

df.head()

dataset=df.values
x=dataset[:,1:6]
y=dataset[:,0]

x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.3, random_state=seed)

model=Sequential()
model.add(Dense(30, input_dim=5, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(4, activation='relu'))
model.add(Dense(1))

model.compile(loss='mean_squared_error', optimizer='adam')

model.fit(x_train, y_train, epochs=500, batch_size=20)

y_prediction=model.predict(x_test).flatten()
for i in range(20):
    label=y_test[i]
    prediction=y_prediction[i]
    print("실제DISP : {:.3f}, 예상DISP : {:.3f}".format(label, prediction))

model.save('it.h5')

model.to_json()
with open('model.json', 'w') as f:
    f.write(model.to_json())

from keras.models import load_model

model = load_model('./it.h5')

ship_element=np.array([141.1,24.2,13.2,9.714,0.9866])

ship_element=np.expand_dims(ship_element, axis=0)

model.predict(ship_element)

import os
import os.path as osp
import argparse

import tensorflow as tf

from keras.models import load_model
from keras import backend as K

def convertGraph( modelPath, outdir, numoutputs, prefix, name):

    if not os.path.isdir(outdir):
        os.mkdir(outdir)

    K.set_learning_phase(0)

    net_model = load_model(modelPath)


    pred = [None]*numoutputs
    pred_node_names = [None]*numoutputs
    for i in range(numoutputs):
        pred_node_names[i] = prefix+'_'+str(i)
        pred[i] = tf.identity(net_model.output[i], name=pred_node_names[i])
    print('Output nodes names are: ', pred_node_names)

    sess = K.get_session()
    
    f = 'graph_def_for_reference.pb.ascii'
    tf.train.write_graph(sess.graph.as_graph_def(), outdir, f, as_text=True)
    print('Saved the graph definition in ascii format at: ', osp.join(outdir, f))

    from tensorflow.python.framework import graph_util
    from tensorflow.python.framework import graph_io
    constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), pred_node_names)
    graph_io.write_graph(constant_graph, outdir, name, as_text=False)
    print('Saved the constant graph (ready for inference) at: ', osp.join(outdir, name))

convertGraph("it.h5","./",2,"k2tfout","output_graph.pb")