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Oceania2018Oceania2018
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tensorflow 1.13rc2
ImageRecognition in prograss.
1 parent 1088e1f commit be89140

7 files changed

Lines changed: 194 additions & 4 deletions

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‎src/TensorFlowNET.Core/Tensors/Tensor.Creation.cs‎

Lines changed: 21 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -54,9 +54,27 @@ private IntPtr Allocate(NDArray nd)
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case "Double":
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Marshal.Copy(nd.Data<double>(), 0, dotHandle, nd.size);
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break;
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case "Byte":
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var bb = nd.Data<byte>();
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var bytes = Marshal.AllocHGlobal(bb.Length) ;
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ulong bytes_len = c_api.TF_StringEncodedSize((ulong)bb.Length);
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var dataTypeByte = ToTFDataType(nd.dtype);
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// shape
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var dims2 = nd.shape.Select(x => (long)x).ToArray();
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var tfHandle2 = c_api.TF_AllocateTensor(dataTypeByte,
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dims2,
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nd.ndim,
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bytes_len + sizeof(Int64));
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dotHandle = c_api.TF_TensorData(tfHandle2);
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Marshal.WriteInt64(dotHandle, 0);
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c_api.TF_StringEncode(bytes, (ulong)bb.Length, dotHandle + sizeof(Int64), bytes_len, status);
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return tfHandle2;
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case "String":
58-
var str = nd.Data<string>()[0];
59-
ulong dst_len = c_api.TF_StringEncodedSize((ulong)str.Length);
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string ss = nd.Data<string>()[0];
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var str = Marshal.StringToHGlobalAnsi(ss);
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ulong dst_len = c_api.TF_StringEncodedSize((ulong)ss.Length);
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var dataType1 = ToTFDataType(nd.dtype);
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// shape
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var dims1 = nd.shape.Select(x => (long)x).ToArray();
@@ -68,7 +86,7 @@ private IntPtr Allocate(NDArray nd)
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dotHandle = c_api.TF_TensorData(tfHandle1);
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Marshal.WriteInt64(dotHandle, 0);
71-
c_api.TF_StringEncode(str, (ulong)str.Length, dotHandle + sizeof(Int64), dst_len, status);
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c_api.TF_StringEncode(str, (ulong)ss.Length, dotHandle + sizeof(Int64), dst_len, status);
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return tfHandle1;
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default:
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throw new NotImplementedException("Marshal.Copy failed.");

‎src/TensorFlowNET.Core/Tensors/Tensor.cs‎

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -164,6 +164,7 @@ public TF_DataType ToTFDataType(Type type)
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return TF_DataType.TF_FLOAT;
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case "Double":
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return TF_DataType.TF_DOUBLE;
167+
case "Byte":
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case "String":
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return TF_DataType.TF_STRING;
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default:

‎src/TensorFlowNET.Core/Tensors/c_api.tensor.cs‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -120,7 +120,7 @@ public partial class c_api
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/// <param name="status">TF_Status*</param>
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/// <returns>On success returns the size in bytes of the encoded string.</returns>
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[DllImport(TensorFlowLibName)]
123-
public static extern ulong TF_StringEncode(string src, ulong src_len, IntPtr dst, ulong dst_len, IntPtr status);
123+
public static extern ulong TF_StringEncode(IntPtr src, ulong src_len, IntPtr dst, ulong dst_len, IntPtr status);
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/// <summary>
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/// Decode a string encoded using TF_StringEncode.

‎src/TensorFlowNET.Core/runtimes/win-x64/native/tensorflow.dll‎

-512 Bytes
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‎src/TensorFlowNET.Core/tf.cs‎

Lines changed: 7 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -57,5 +57,12 @@ public static Session Session()
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defaultSession = new Session();
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return defaultSession;
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}
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public static Session Session(Graph graph)
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{
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g = graph;
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defaultSession = new Session();
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return defaultSession;
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}
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}
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}

‎test/TensorFlowNET.Examples/ImageRecognition.cs‎

Lines changed: 31 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,31 @@
1+
using System;
2+
using System.Collections.Generic;
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using System.IO;
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using System.IO.Compression;
5+
using System.Linq;
6+
using System.Net;
7+
using System.Text;
8+
using Tensorflow;
9+
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namespace TensorFlowNET.Examples
11+
{
12+
public class ImageRecognition : Python, IExample
13+
{
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public void Run()
15+
{
16+
var graph = new Graph();
17+
// 从文件加载序列化的GraphDef
18+
//导入GraphDef
19+
graph.Import("tmp/tensorflow_inception_graph.pb");
20+
with<Session>(tf.Session(graph), sess =>
21+
{
22+
var labels = File.ReadAllLines("tmp/imagenet_comp_graph_label_strings.txt");
23+
var files = Directory.GetFiles("img");
24+
foreach(var file in files)
25+
{
26+
var tensor = new Tensor(File.ReadAllBytes(file));
27+
}
28+
});
29+
}
30+
}
31+
}

‎test/TensorFlowNET.Examples/python/basic_text_classification.py‎

Lines changed: 133 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,133 @@
1+

2+
from __future__ import absolute_import, division, print_function
3+
4+
import tensorflow as tf
5+
from tensorflow import keras
6+
7+
import numpy as np
8+
9+
print(tf.__version__)
10+
11+
imdb = keras.datasets.imdb
12+
13+
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
14+
15+
print("Training entries: {}, labels: {}".format(len(train_data), len(train_labels)))
16+
print(train_data[0])
17+
len(train_data[0]), len(train_data[1])
18+
19+
# A dictionary mapping words to an integer index
20+
word_index = imdb.get_word_index()
21+
22+
# The first indices are reserved
23+
word_index = {k:(v+3) for k,v in word_index.items()}
24+
word_index["<PAD>"] = 0
25+
word_index["<START>"] = 1
26+
word_index["<UNK>"] = 2 # unknown
27+
word_index["<UNUSED>"] = 3
28+
29+
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
30+
31+
def decode_review(text):
32+
return ' '.join([reverse_word_index.get(i, '?') for i in text])
33+
34+
decode_review(train_data[0])
35+
36+
37+
train_data = keras.preprocessing.sequence.pad_sequences(train_data,
38+
value=word_index["<PAD>"],
39+
padding='post',
40+
maxlen=256)
41+
42+
test_data = keras.preprocessing.sequence.pad_sequences(test_data,
43+
value=word_index["<PAD>"],
44+
padding='post',
45+
maxlen=256)
46+
47+
48+
print(train_data[0])
49+
50+
# input shape is the vocabulary count used for the movie reviews (10,000 words)
51+
vocab_size = 10000
52+
53+
model = keras.Sequential()
54+
model.add(keras.layers.Embedding(vocab_size, 16))
55+
model.add(keras.layers.GlobalAveragePooling1D())
56+
model.add(keras.layers.Dense(16, activation=tf.nn.relu))
57+
model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))
58+
59+
model.summary()
60+
61+
model.compile(optimizer='adam',
62+
loss='binary_crossentropy',
63+
metrics=['accuracy'])
64+
65+
66+
x_val = train_data[:10000]
67+
partial_x_train = train_data[10000:]
68+
69+
y_val = train_labels[:10000]
70+
partial_y_train = train_labels[10000:]
71+
72+
history = model.fit(partial_x_train,
73+
partial_y_train,
74+
epochs=40,
75+
batch_size=512,
76+
validation_data=(x_val, y_val),
77+
verbose=1)
78+
79+
results = model.evaluate(test_data, test_labels)
80+
81+
# serialize model to JSON
82+
model_json = model.to_json()
83+
with open("model.json", "w") as json_file:
84+
json_file.write(model_json)
85+
# serialize weights to HDF5
86+
model.save_weights("model.h5")
87+
print("Saved model to disk")
88+
89+
# load json and create model
90+
json_file = open('model.json', 'r')
91+
loaded_model_json = json_file.read()
92+
json_file.close()
93+
loaded_model = model_from_json(loaded_model_json)
94+
# load weights into new model
95+
loaded_model.load_weights("model.h5")
96+
print("Loaded model from disk")
97+
98+
print(results)
99+
100+
history_dict = history.history
101+
history_dict.keys()
102+
103+
import matplotlib.pyplot as plt
104+
105+
acc = history_dict['acc']
106+
val_acc = history_dict['val_acc']
107+
loss = history_dict['loss']
108+
val_loss = history_dict['val_loss']
109+
110+
epochs = range(1, len(acc) + 1)
111+
112+
# "bo" is for "blue dot"
113+
plt.plot(epochs, loss, 'bo', label='Training loss')
114+
# b is for "solid blue line"
115+
plt.plot(epochs, val_loss, 'b', label='Validation loss')
116+
plt.title('Training and validation loss')
117+
plt.xlabel('Epochs')
118+
plt.ylabel('Loss')
119+
plt.legend()
120+
121+
plt.show()
122+
123+
124+
plt.clf() # clear figure
125+
126+
plt.plot(epochs, acc, 'bo', label='Training acc')
127+
plt.plot(epochs, val_acc, 'b', label='Validation acc')
128+
plt.title('Training and validation accuracy')
129+
plt.xlabel('Epochs')
130+
plt.ylabel('Accuracy')
131+
plt.legend()
132+
133+
plt.show()

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