Sebastian Raschka, 2015
Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).
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%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,matplotlib,theano,keras
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# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
Depending on your system setup, it is typically sufficient to install Theano via
pip install Theano
For more help with the installation, please see: http://deeplearning.net/software/theano/install.html
Introducing the TensorType variables. For a complete list, see http://deeplearning.net/software/theano/library/tensor/basic.html#all-fully-typed-constructors
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import theano
from theano import tensor as T
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# initialize
x1 = T.scalar()
w1 = T.scalar()
w0 = T.scalar()
z1 = w1 * x1 + w0
# compile
net_input = theano.function(inputs=[w1, x1, w0], outputs=z1)
# execute
net_input(2.0, 1.0, 0.5)
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Configuring Theano. For more options, see
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print(theano.config.floatX)
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theano.config.floatX = 'float32'
To change the float type globally, execute
export THEANO_FLAGS=floatX=float32
in your bash shell. Or execute Python script as
THEANO_FLAGS=floatX=float32 python your_script.pyRunning Theano on GPU(s). For prerequisites, please see: http://deeplearning.net/software/theano/tutorial/using_gpu.html
Note that float32 is recommended for GPUs; float64 on GPUs is currently still relatively slow.
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print(theano.config.device)
You can run a Python script on CPU via:
THEANO_FLAGS=device=cpu,floatX=float64 python your_script.py
or GPU via
THEANO_FLAGS=device=gpu,floatX=float32 python your_script.pyIt may also be convenient to create a .theanorc file in your home directory to make those configurations permanent. For example, to always use float32, execute
echo -e "\n[global]\nfloatX=float32\n" >> ~/.theanorc
Or, create a .theanorc file manually with the following contents
[global]
floatX = float32
device = gpu
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import numpy as np
# initialize
x = T.fmatrix(name='x')
x_sum = T.sum(x, axis=0)
# compile
calc_sum = theano.function(inputs=[x], outputs=x_sum)
# execute (Python list)
ary = [[1, 2, 3], [1, 2, 3]]
print('Column sum:', calc_sum(ary))
# execute (NumPy array)
ary = np.array([[1, 2, 3], [1, 2, 3]], dtype=theano.config.floatX)
print('Column sum:', calc_sum(ary))
Updating shared arrays. More info about memory management in Theano can be found here: http://deeplearning.net/software/theano/tutorial/aliasing.html
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# initialize
x = T.fmatrix(name='x')
w = theano.shared(np.asarray([[0.0, 0.0, 0.0]],
dtype=theano.config.floatX))
z = x.dot(w.T)
update = [[w, w + 1.0]]
# compile
net_input = theano.function(inputs=[x],
updates=update,
outputs=z)
# execute
data = np.array([[1, 2, 3]], dtype=theano.config.floatX)
for i in range(5):
print('z%d:' % i, net_input(data))
We can use the givens variable to insert values into the graph before compiling it. Using this approach we can reduce the number of transfers from RAM (via CPUs) to GPUs to speed up learning with shared variables. If we use inputs, a datasets is transferred from the CPU to the GPU multiple times, for example, if we iterate over a dataset multiple times (epochs) during gradient descent. Via givens, we can keep the dataset on the GPU if it fits (e.g., a mini-batch).
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# initialize
data = np.array([[1, 2, 3]],
dtype=theano.config.floatX)
x = T.fmatrix(name='x')
w = theano.shared(np.asarray([[0.0, 0.0, 0.0]],
dtype=theano.config.floatX))
z = x.dot(w.T)
update = [[w, w + 1.0]]
# compile
net_input = theano.function(inputs=[],
updates=update,
givens={x: data},
outputs=z)
# execute
for i in range(5):
print('z:', net_input())
Creating some training data.
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import numpy as np
X_train = np.asarray([[0.0], [1.0], [2.0], [3.0], [4.0],
[5.0], [6.0], [7.0], [8.0], [9.0]],
dtype=theano.config.floatX)
y_train = np.asarray([1.0, 1.3, 3.1, 2.0, 5.0,
6.3, 6.6, 7.4, 8.0, 9.0],
dtype=theano.config.floatX)
Implementing the training function.
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import theano
from theano import tensor as T
import numpy as np
def train_linreg(X_train, y_train, eta, epochs):
costs = []
# Initialize arrays
eta0 = T.fscalar('eta0')
y = T.fvector(name='y')
X = T.fmatrix(name='X')
w = theano.shared(np.zeros(
shape=(X_train.shape[1] + 1),
dtype=theano.config.floatX),
name='w')
# calculate cost
net_input = T.dot(X, w[1:]) + w[0]
errors = y - net_input
cost = T.sum(T.pow(errors, 2))
# perform gradient update
gradient = T.grad(cost, wrt=w)
update = [(w, w - eta0 * gradient)]
# compile model
train = theano.function(inputs=[eta0],
outputs=cost,
updates=update,
givens={X: X_train,
y: y_train,})
for _ in range(epochs):
costs.append(train(eta))
return costs, w
Plotting the sum of squared errors cost vs epochs.
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%matplotlib inline
import matplotlib.pyplot as plt
costs, w = train_linreg(X_train, y_train, eta=0.001, epochs=10)
plt.plot(range(1, len(costs)+1), costs)
plt.tight_layout()
plt.xlabel('Epoch')
plt.ylabel('Cost')
plt.tight_layout()
# plt.savefig('./figures/cost_convergence.png', dpi=300)
plt.show()
Making predictions.
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def predict_linreg(X, w):
Xt = T.matrix(name='X')
net_input = T.dot(Xt, w[1:]) + w[0]
predict = theano.function(inputs=[Xt], givens={w: w}, outputs=net_input)
return predict(X)
plt.scatter(X_train, y_train, marker='s', s=50)
plt.plot(range(X_train.shape[0]),
predict_linreg(X_train, w),
color='gray',
marker='o',
markersize=4,
linewidth=3)
plt.xlabel('x')
plt.ylabel('y')
plt.tight_layout()
# plt.savefig('./figures/linreg.png', dpi=300)
plt.show()
The logistic function, often just called "sigmoid function" is in fact a special case of a sigmoid function.
Net input $z$: $$z = w_1x_{1} + \dots + w_mx_{m} = \sum_{j=1}^{m} x_{j}w_{j} \\ = \mathbf{w}^T\mathbf{x}$$
Logistic activation function:
$$\phi_{logistic}(z) = \frac{1}{1 + e^{-z}}$$Output range: (0, 1)
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# note that first element (X[0] = 1) to denote bias unit
X = np.array([[1, 1.4, 1.5]])
w = np.array([0.0, 0.2, 0.4])
def net_input(X, w):
z = X.dot(w)
return z
def logistic(z):
return 1.0 / (1.0 + np.exp(-z))
def logistic_activation(X, w):
z = net_input(X, w)
return logistic(z)
print('P(y=1|x) = %.3f' % logistic_activation(X, w)[0])
Now, imagine a MLP perceptron with 3 hidden units + 1 bias unit in the hidden unit. The output layer consists of 3 output units.
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# W : array, shape = [n_output_units, n_hidden_units+1]
# Weight matrix for hidden layer -> output layer.
# note that first column (A[:][0] = 1) are the bias units
W = np.array([[1.1, 1.2, 1.3, 0.5],
[0.1, 0.2, 0.4, 0.1],
[0.2, 0.5, 2.1, 1.9]])
# A : array, shape = [n_hidden+1, n_samples]
# Activation of hidden layer.
# note that first element (A[0][0] = 1) is for the bias units
A = np.array([[1.0],
[0.1],
[0.3],
[0.7]])
# Z : array, shape = [n_output_units, n_samples]
# Net input of output layer.
Z = W.dot(A)
y_probas = logistic(Z)
print('Probabilities:\n', y_probas)
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y_class = np.argmax(Z, axis=0)
print('predicted class label: %d' % y_class[0])
The softmax function is a generalization of the logistic function and allows us to compute meaningful class-probalities in multi-class settings (multinomial logistic regression).
$$P(y=j|z) =\phi_{softmax}(z) = \frac{e^{z_j}}{\sum_{k=1}^K e^{z_k}}$$the input to the function is the result of K distinct linear functions, and the predicted probability for the j'th class given a sample vector x is:
Output range: (0, 1)
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def softmax(z):
return np.exp(z) / np.sum(np.exp(z))
def softmax_activation(X, w):
z = net_input(X, w)
return sigmoid(z)
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y_probas = softmax(Z)
print('Probabilities:\n', y_probas)
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y_probas.sum()
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y_class = np.argmax(Z, axis=0)
y_class
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Another special case of a sigmoid function, it can be interpreted as a rescaled version of the logistic function.
$$\phi_{tanh}(z) = \frac{e^{z}-e^{-z}}{e^{z}+e^{-z}}$$Output range: (-1, 1)
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def tanh(z):
e_p = np.exp(z)
e_m = np.exp(-z)
return (e_p - e_m) / (e_p + e_m)
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import matplotlib.pyplot as plt
%matplotlib inline
z = np.arange(-5, 5, 0.005)
log_act = logistic(z)
tanh_act = tanh(z)
# alternatives:
# from scipy.special import expit
# log_act = expit(z)
# tanh_act = np.tanh(z)
plt.ylim([-1.5, 1.5])
plt.xlabel('net input $z$')
plt.ylabel('activation $\phi(z)$')
plt.axhline(1, color='black', linestyle='--')
plt.axhline(0.5, color='black', linestyle='--')
plt.axhline(0, color='black', linestyle='--')
plt.axhline(-1, color='black', linestyle='--')
plt.plot(z, tanh_act,
linewidth=2,
color='black',
label='tanh')
plt.plot(z, log_act,
linewidth=2,
color='lightgreen',
label='logistic')
plt.legend(loc='lower right')
plt.tight_layout()
# plt.savefig('./figures/activation.png', dpi=300)
plt.show()
1) Download the 4 MNIST datasets from http://yann.lecun.com/exdb/mnist/
2) Unzip those files
3 Copy the unzipped files to a directory ./mnist
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import os
import struct
import numpy as np
def load_mnist(path, kind='train'):
"""Load MNIST data from `path`"""
labels_path = os.path.join(path,
'%s-labels-idx1-ubyte'
% kind)
images_path = os.path.join(path,
'%s-images-idx3-ubyte'
% kind)
with open(labels_path, 'rb') as lbpath:
magic, n = struct.unpack('>II',
lbpath.read(8))
labels = np.fromfile(lbpath,
dtype=np.uint8)
with open(images_path, 'rb') as imgpath:
magic, num, rows, cols = struct.unpack(">IIII",
imgpath.read(16))
images = np.fromfile(imgpath,
dtype=np.uint8).reshape(len(labels), 784)
return images, labels
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X_train, y_train = load_mnist('mnist', kind='train')
print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))
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X_test, y_test = load_mnist('mnist', kind='t10k')
print('Rows: %d, columns: %d' % (X_test.shape[0], X_test.shape[1]))
Once you have Theano installed, Keras can be installed via
pip install KerasIn order to run the following code via GPU, you can execute the Python script that was placed in this directory via
THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python mnist_keras_mlp.py
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import theano
theano.config.floatX = 'float32'
X_train = X_train.astype(theano.config.floatX)
X_test = X_test.astype(theano.config.floatX)
One-hot encoding of the class variable:
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from keras.utils import np_utils
print('First 3 labels: ', y_train[:3])
y_train_ohe = np_utils.to_categorical(y_train)
print('\nFirst 3 labels (one-hot):\n', y_train_ohe[:3])
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from keras.models import Sequential
from keras.layers.core import Dense
from keras.optimizers import SGD
np.random.seed(1)
model = Sequential()
model.add(Dense(input_dim=X_train.shape[1],
output_dim=50,
init='uniform',
activation='tanh'))
model.add(Dense(input_dim=50,
output_dim=50,
init='uniform',
activation='tanh'))
model.add(Dense(input_dim=50,
output_dim=y_train_ohe.shape[1],
init='uniform',
activation='softmax'))
sgd = SGD(lr=0.001, decay=1e-7, momentum=.9)
model.compile(loss='categorical_crossentropy', optimizer=sgd)
model.fit(X_train, y_train_ohe,
nb_epoch=50,
batch_size=300,
verbose=1,
validation_split=0.1,
show_accuracy=True)
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y_train_pred = model.predict_classes(X_train, verbose=0)
print('First 3 predictions: ', y_train_pred[:3])
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train_acc = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0]
print('Training accuracy: %.2f%%' % (train_acc * 100))
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y_test_pred = model.predict_classes(X_test, verbose=0)
test_acc = np.sum(y_test == y_test_pred, axis=0) / X_test.shape[0]
print('Test accuracy: %.2f%%' % (test_acc * 100))
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