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(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)
