docker-python/tests/test_keras.py at master · Technology-Development/docker-python · GitHub

History

82 lines (61 loc) · 3.07 KB

Raw

import unittest

import keras

import numpy as np

import pandas as pd

from keras.models import Sequential

from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, LSTM

from keras.optimizers import RMSprop, SGD

from keras.utils.np_utils import to_categorical

from common import gpu_test

class TestKeras(unittest.TestCase):

def test_train(self):

train = pd.read_csv("/input/tests/data/train.csv")

x_train = train.iloc[:,1:].values.astype('float32')

y_train = to_categorical(train.iloc[:,0].astype('int32'))

model = Sequential()

model.add(Dense(units=10, input_dim=784, activation='softmax'))

model.compile(

loss='categorical_crossentropy',

optimizer=RMSprop(lr=0.001),

metrics=['accuracy'])

model.fit(x_train, y_train, epochs=1, batch_size=32)

# Uses convnet which depends on libcudnn when running on GPU

def test_conv2d(self):

# Generate dummy data

x_train = np.random.random((100, 100, 100, 3))

y_train = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)

x_test = np.random.random((20, 100, 100, 3))

y_test = keras.utils.to_categorical(np.random.randint(10, size=(20, 1)), num_classes=10)

model = Sequential()

# input: 100x100 images with 3 channels -> (100, 100, 3) tensors.

# this applies 32 convolution filters of size 3x3 each.

model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))

model.add(Conv2D(32, (3, 3), activation='relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), activation='relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))

model.add(Flatten())

model.add(Dense(256, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(10, activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

# This throws if libcudnn is not properly installed with on a GPU

model.compile(loss='categorical_crossentropy', optimizer=sgd)

model.fit(x_train, y_train, batch_size=32, epochs=1)

model.evaluate(x_test, y_test, batch_size=32)

def test_lstm(self):

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

y_train = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)

x_test = np.random.random((20, 100, 100))

y_test = keras.utils.to_categorical(np.random.randint(10, size=(20, 1)), num_classes=10)

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

model = Sequential()

model.add(LSTM(32, return_sequences=True, input_shape=(100, 100)))

model.add(Flatten())

model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer=sgd)

model.fit(x_train, y_train, batch_size=32, epochs=1)

model.evaluate(x_test, y_test, batch_size=32)

docker-python/tests/test_keras.py at master · Technology-Development/docker-python · GitHub

Expand file tree

Breadcrumbs

test_keras.py

Latest commit

History

Breadcrumbs

test_keras.py

File metadata and controls