docker-python/tests/test_tensorflow.py at main · MIBlue119/docker-python · GitHub

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import unittest

import os.path

import numpy as np

import tensorflow as tf

from common import gpu_test

class TestTensorflow(unittest.TestCase):

def test_addition(self):

result = tf.add([1, 2], [3, 4])

self.assertEqual([2], result.shape)

def test_conv2d(self):

input = tf.random.normal([1,2,2,1])

filter = tf.random.normal([1,1,1,1])

result = tf.nn.conv2d(input, filter, strides=[1, 1, 1, 1], padding='SAME')

self.assertEqual(4, len(result.shape))

def test_tf_keras(self):

x_train = np.random.random((100, 28, 28))

y_train = np.random.randint(10, size=(100, 1))

x_test = np.random.random((20, 28, 28))

y_test = np.random.randint(10, size=(20, 1))

model = tf.keras.models.Sequential([

tf.keras.layers.Flatten(input_shape=(28, 28)),

tf.keras.layers.Dense(128, activation='relu'),

tf.keras.layers.Dropout(0.2),

tf.keras.layers.Dense(10, activation='softmax')

])

model.compile(

optimizer='adam',

loss='sparse_categorical_crossentropy',

metrics=['accuracy'])

model.fit(x_train, y_train, epochs=1)

result = model.evaluate(x_test, y_test)

self.assertEqual(2, len(result))

# exercices pydot path.

tf.keras.utils.plot_model(model, to_file="tf_plot_model.png")

self.assertTrue(os.path.isfile("tf_plot_model.png"))

def test_lstm(self):

x_train = np.random.random((100, 28, 28))

y_train = np.random.randint(10, size=(100, 1))

x_test = np.random.random((20, 28, 28))

y_test = np.random.randint(10, size=(20, 1))

model = tf.keras.Sequential([

tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, input_shape=(28, 28))),

tf.keras.layers.Dense(64, activation='relu'),

tf.keras.layers.Dense(1, activation='sigmoid')

])

model.compile(

loss='binary_crossentropy',

optimizer='adam',

metrics=['accuracy'])

model.fit(x_train, y_train, epochs=1)

result = model.evaluate(x_test, y_test)

self.assertEqual(2, len(result))

@gpu_test

def test_gpu(self):

with tf.device('/gpu:0'):

m1 = tf.constant([2.0, 3.0], shape=[1, 2], name='a')

m2 = tf.constant([3.0, 4.0], shape=[2, 1], name='b')

result = tf.matmul(m1, m2)

self.assertEqual([1, 1], result.shape)

@gpu_test

def test_is_built_with_cuda(self):

self.assertTrue(tf.test.is_built_with_cuda())

@gpu_test

def test_is_gpu_available(self):

self.assertTrue(tf.test.is_gpu_available(cuda_only=True))

docker-python/tests/test_tensorflow.py at main · MIBlue119/docker-python · GitHub

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