#!/usr/bin/env python
# coding: utf-8

# # CNN (keras example) on mnist (from keras's examples) with convolution visualization
# 
# This is https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py

# ## For keras < v1

# In[1]:


import os
os.environ['THEANO_FLAGS']='mode=FAST_RUN,device=gpu,floatX=float32'
# This gives a 'perform' error in compile
#os.environ['THEANO_FLAGS']='mode=FAST_COMPILE,device=gpu1,floatX=float32'
#os.environ['THEANO_FLAGS']='device=gpu0'


# In[2]:


import theano
print theano.config.device


# In[3]:


from __future__ import absolute_import
from __future__ import print_function
import pylab as pl
import matplotlib.cm as cm
import numpy as np
np.random.seed(1337) # for reproducibility

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.utils import np_utils

'''
    Train a simple convnet on the MNIST dataset.

    Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python mnist_cnn.py

    Get to 99.25% test accuracy after 12 epochs (there is still a lot of margin for parameter tuning).
    16 seconds per epoch on a GRID K520 GPU.
'''


# In[4]:


np.set_printoptions(precision=5, suppress=True)


# In[5]:


get_ipython().run_line_magic('matplotlib', 'inline')


# In[6]:


nb_classes = 10

# the data, shuffled and split between tran and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_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
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)


# In[7]:


i = 4600
pl.imshow(X_train[i, 0], interpolation='nearest', cmap=cm.binary)
print("label : ", Y_train[i,:])


# In[8]:


model = Sequential()

model.add(Convolution2D(32, 1, 3, 3, border_mode='full')) 
convout1 = Activation('relu')
model.add(convout1)
model.add(Convolution2D(32, 32, 3, 3))

convout2 = Activation('relu')
model.add(convout2)
model.add(MaxPooling2D(poolsize=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(32*196, 128))
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(128, nb_classes))
model.add(Activation('softmax'))

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


# In[9]:


WEIGHTS_FNAME = 'mnist_cnn_weights.hdf'
if True and os.path.exists(WEIGHTS_FNAME):
    # Just change the True to false to force re-training
    print('Loading existing weights')
    model.load_weights(WEIGHTS_FNAME)
else:
    batch_size = 128
    nb_epoch = 12
    model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
              show_accuracy=True, verbose=1, validation_data=(X_test, Y_test))
    model.save_weights(WEIGHTS_FNAME)
score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])

    


# In[10]:


print(model.predict(X_test[1:5]))
print(Y_test[1:5])


# In[11]:


Y_pred = model.predict(X_test)
# Convert one-hot to index
y_pred = np.argmax(Y_pred, axis=1)


# In[12]:


from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))


# ## Convolution visualizations

# In[13]:


convout1_f = theano.function([model.get_input(train=False)], convout1.get_output(train=False))
#convout2_f = theano.function([model.get_input(train=False)], convout2.get_output(train=False))


# In[14]:


# utility functions
from mpl_toolkits.axes_grid1 import make_axes_locatable

def nice_imshow(ax, data, vmin=None, vmax=None, cmap=None):
    """Wrapper around pl.imshow"""
    if cmap is None:
        cmap = cm.jet
    if vmin is None:
        vmin = data.min()
    if vmax is None:
        vmax = data.max()
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.05)
    im = ax.imshow(data, vmin=vmin, vmax=vmax, interpolation='nearest', cmap=cmap)
    pl.colorbar(im, cax=cax)


# In[15]:


i = 4600

# Visualize the first layer of convolutions on an input image
X = X_test[i:i+1]

pl.figure()
pl.title('input')
nice_imshow(pl.gca(), np.squeeze(X), vmin=0, vmax=1, cmap=cm.binary)


# In[16]:


import numpy.ma as ma
def make_mosaic(imgs, nrows, ncols, border=1):
    """
    Given a set of images with all the same shape, makes a
    mosaic with nrows and ncols
    """
    nimgs = imgs.shape[0]
    imshape = imgs.shape[1:]
    
    mosaic = ma.masked_all((nrows * imshape[0] + (nrows - 1) * border,
                            ncols * imshape[1] + (ncols - 1) * border),
                            dtype=np.float32)
    
    paddedh = imshape[0] + border
    paddedw = imshape[1] + border
    for i in xrange(nimgs):
        row = int(np.floor(i / ncols))
        col = i % ncols
        
        mosaic[row * paddedh:row * paddedh + imshape[0],
               col * paddedw:col * paddedw + imshape[1]] = imgs[i]
    return mosaic

#pl.imshow(make_mosaic(np.random.random((9, 10, 10)), 3, 3, border=1))


# In[17]:


# Visualize weights
W = model.layers[0].W.get_value(borrow=True)
W = np.squeeze(W)
print("W shape : ", W.shape)

pl.figure(figsize=(15, 15))
pl.title('conv1 weights')
nice_imshow(pl.gca(), make_mosaic(W, 6, 6), cmap=cm.binary)


# In[18]:


# Visualize convolution result (after activation)
C1 = convout1_f(X)
C1 = np.squeeze(C1)
print("C1 shape : ", C1.shape)

pl.figure(figsize=(15, 15))
pl.suptitle('convout1')
nice_imshow(pl.gca(), make_mosaic(C1, 6, 6), cmap=cm.binary)