import keras
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense,Activation,Flatten,Dropout,Convolution2D,MaxPooling2D
from keras.utils import np_utils
from keras.optimizers import RMSprop
from skimage import io

nb_classes=10
batch_size=128
####因为是卷积神经网络，输入数据的格式是图像格式，所以要进行reshape
train_X = io.imread("E:\\WaySign\\0_0_colorrgb0.ppm")
train_x=np.reshape(train_X,(train_X.shape[0],32,32,1))
# test_x=np.reshape(test_X,(test_X.shape[0],28,28,1))
# train_y=np_utils.to_categorical(train_Y,nb_classes)
# test_y=np_utils.to_categorical(test_Y,nb_classes)

print(train_y.shape,'\n',test_y.shape)

print(train_x.shape,'\n',test_x.shape)

train_x[:,:,:,0].shape

###reshape后的数据显示
import matplotlib.pyplot as plt
%matplotlib inline
f,a=plt.subplots(1,10,figsize=(10,5))
for i in range(10):
a[i].imshow(train_x[i,:,:,0],cmap='gray')
print(train_Y[i])

####establish a convolution nerual network
model=Sequential()

####Convolution layer 1
model.add(Convolution2D(filters=32,kernel_size=(3,3),input_shape=(28,28,1),strides=(1,1),\
padding='same',activation='relu'))

#####pooling layer with dropout
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
model.add(Dropout(0.2))

####Convolution layer 2
model.add(Convolution2D(filters=64,kernel_size=(3,3),strides=(1,1),padding='same',\
activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
model.add(Dropout(0.2))

####Convolution layer 3
model.add(Convolution2D(filters=128,kernel_size=(3,3),strides=(1,1),padding='same',\
activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
model.add(Flatten())###理解扁平化
model.add(Dropout(0.2))

#model.add(Flatten())?

####fully connected layer 1 (fc layer)
model.add(Dense(output_dim=625,activation='relu'))
model.add(Dropout(0.5))

####fully connected layer 2 (fc layer)
model.add(Dense(output_dim=10,activation='softmax'))
model.summary()

model.compile(optimizer=RMSprop(lr=0.001,rho=0.9),loss="categorical_crossentropy",\
metrics=['accuracy'])
import time
start_time=time.time()
model.fit(train_x,train_y,epochs=30,batch_size=128,verbose=1)
end_time=time.time()
print("running time:%.2f"%(end_time-start_time))

evaluation=model.evaluate(test_x,test_y,batch_size=128,verbose=1)
print("model loss:%.4f"%(evaluation[0]),"model accuracy:%.4f"%(evaluation[1]))

# https://github.com/fchollet/keras/issues/431
def get_activations(model, model_inputs, print_shape_only=True, layer_name=None):
import keras.backend as K
print('----- activations -----')
activations = []
inp = model.input

model_multi_inputs_cond = True
if not isinstance(inp, list):
# only one input! let's wrap it in a list.
inp = [inp]
model_multi_inputs_cond = False

outputs = [layer.output for layer in model.layers if
layer.name == layer_name or layer_name is None] # all layer outputs

funcs = [K.function(inp + [K.learning_phase()], [out]) for out in outputs] # evaluation functions

if model_multi_inputs_cond:
list_inputs = []
list_inputs.extend(model_inputs)
list_inputs.append(1.)
else:
list_inputs = [model_inputs, 1.]

# Learning phase. 1 = Test mode (no dropout or batch normalization)
# layer_outputs = [func([model_inputs, 1.])[0] for func in funcs]
layer_outputs = [func(list_inputs)[0] for func in funcs]
for layer_activations in layer_outputs:
activations.append(layer_activations)
if print_shape_only:
print(layer_activations.shape)
else:
print(layer_activations)
return activations

# https://github.com/philipperemy/keras-visualize-activations/blob/master/read_activations.py
def display_activations(activation_maps):
import numpy as np
import matplotlib.pyplot as plt
"""
(1, 28, 28, 32)
(1, 14, 14, 32)
(1, 14, 14, 32)
(1, 14, 14, 64)
(1, 7, 7, 64)
(1, 7, 7, 64)
(1, 7, 7, 128)
(1, 3, 3, 128)
(1, 1152)
(1, 1152)
(1, 625)
(1, 625)
(1, 10)
"""
batch_size = activation_maps[0].shape[0]
assert batch_size == 1, 'One image at a time to visualize.'
for i, activation_map in enumerate(activation_maps):
print('Displaying activation map {}'.format(i))
shape = activation_map.shape
if len(shape) == 4:
activations = np.hstack(np.transpose(activation_map[0], (2, 0, 1)))
elif len(shape) == 2:
# try to make it square as much as possible. we can skip some activations.
activations = activation_map[0]
num_activations = len(activations)
if num_activations > 1024: # too hard to display it on the screen.
square_param = int(np.floor(np.sqrt(num_activations)))
activations = activations[0: square_param * square_param]
activations = np.reshape(activations, (square_param, square_param))
else:
activations = np.expand_dims(activations, axis=0)
else:
raise Exception('len(shape) = 3 has not been implemented.')
#plt.imshow(activations, interpolation='None', cmap='binary')
fig, ax = plt.subplots(figsize=(18, 12))
ax.imshow(activations, interpolation='None', cmap='binary')
plt.show()

###One image at a time to visualize.
activations = get_activations(model, (test_x[0,:,:,:])[np.newaxis,:])

(test_x[0,:,:,:])[np.newaxis,:].shape

display_activations(activations)

plt.imshow(test_x[0,:,:,0],cmap='gray')
pred_value=model.predict_classes((test_x[0,:,:,:])[np.newaxis,:],batch_size=1)
print(pred_value)