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)