Thursday, 21 June 2018

Isolate Python working environment using Virtualenv

While working on multiple Python projects, you may face the issue that one project need a version of package that is completely different from the others. Virtualenv supports to resolve this issue by Isolating Python working environments. So dependencies required by the projects are isolated.
1. Install Virtualenv: sudo pip install virtualenv
2. Commands to setup Virtualenv
- mkdir ~/virtualws
- virtualenv ~/virtualws/keras_demo
- cd ~/virtualws/keras_demo/bin
- source activate
3. Test with Keras
- pip install tensorflow
- pip install keras
4. Run demo
Create a test.py file with content and run python test.py
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# 3. Import libraries and modules
import numpy as np
np.random.seed(123)  # for reproducibility
 
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.datasets import mnist
 
# 4. Load pre-shuffled MNIST data into train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
 
# 5. Preprocess input data
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
 
# 6. Preprocess class labels
Y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)
 
# 7. Define model architecture
model = Sequential()

model.add(Convolution2D(32, (3, 3), activation='relu', input_shape=(1,28,28), data_format='channels_first'))
model.add(Convolution2D(32, (3, 3), activation='relu', data_format='channels_first'))
model.add(MaxPooling2D(pool_size=(2,2), data_format='channels_first'))
model.add(Dropout(0.25))
 
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
 
# 8. Compile model
model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
 
# 9. Fit model on training data
model.fit(X_train, Y_train, 
          batch_size=32, nb_epoch=10, verbose=1)
 
# 10. Evaluate model on test data
score = model.evaluate(X_test, Y_test, verbose=0)

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