import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation
Using Theano backend. Using gpu device 0: GeForce GT625M (CNMeM is disabled, cuDNN not available)
%%time
model = Sequential()
model.add(Dense(1,input_dim=784, activation='tanh'))
model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])
data = np.random.random((1000,784))
labels = np.random.randint(2,size=(1000,1))
model.fit(data,labels,nb_epoch=10,batch_size=32)
Epoch 1/10 1000/1000 [==============================] - 0s - loss: 2.9005 - acc: 0.5010 Epoch 2/10 1000/1000 [==============================] - 0s - loss: 4.4020 - acc: 0.3610 Epoch 3/10 1000/1000 [==============================] - 0s - loss: 7.2061 - acc: 0.5130 Epoch 4/10 1000/1000 [==============================] - 0s - loss: 7.7480 - acc: 0.5140 Epoch 5/10 1000/1000 [==============================] - 0s - loss: 7.7480 - acc: 0.5140 Epoch 6/10 1000/1000 [==============================] - 0s - loss: 7.7480 - acc: 0.5140 Epoch 7/10 1000/1000 [==============================] - 0s - loss: 7.7480 - acc: 0.5140 Epoch 8/10 1000/1000 [==============================] - 0s - loss: 7.7480 - acc: 0.5140 Epoch 9/10 1000/1000 [==============================] - 0s - loss: 7.7480 - acc: 0.5140 Epoch 10/10 1000/1000 [==============================] - 0s - loss: 7.7480 - acc: 0.5140 CPU times: user 1.7 s, sys: 416 ms, total: 2.12 s Wall time: 1min 14s
%%time
model = Sequential()
model.add(Dense(1,input_dim=784, activation='linear'))
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
data = np.random.random((1000,784))
labels = np.random.randint(2,size=(1000,1))
model.fit(data,labels,nb_epoch=10,batch_size=32)
Epoch 1/10 1000/1000 [==============================] - 0s - loss: 4.2552 - acc: 0.4020 Epoch 2/10 1000/1000 [==============================] - 0s - loss: 5.8849 - acc: 0.3460 Epoch 3/10 1000/1000 [==============================] - 0s - loss: 3.8482 - acc: 0.4740 Epoch 4/10 1000/1000 [==============================] - 0s - loss: 2.6821 - acc: 0.5090 Epoch 5/10 1000/1000 [==============================] - 0s - loss: 2.6482 - acc: 0.4820 Epoch 6/10 1000/1000 [==============================] - 0s - loss: 2.8464 - acc: 0.4740 Epoch 7/10 1000/1000 [==============================] - 0s - loss: 3.3461 - acc: 0.4530 Epoch 8/10 1000/1000 [==============================] - 0s - loss: 3.3146 - acc: 0.4630 Epoch 9/10 1000/1000 [==============================] - 0s - loss: 3.6025 - acc: 0.4620 Epoch 10/10 1000/1000 [==============================] - 0s - loss: 2.3228 - acc: 0.5060 CPU times: user 1.46 s, sys: 212 ms, total: 1.68 s Wall time: 17.8 s
%%time
model = Sequential()
model.add(Dense(1,input_dim=784, activation='relu'))
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
data = np.random.random((1000,784))
labels = np.random.randint(2,size=(1000,1))
model.fit(data,labels,nb_epoch=50,batch_size=32)
Epoch 1/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 2/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 3/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 4/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 5/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 6/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 7/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 8/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 9/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 10/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 11/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 12/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 13/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 14/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 15/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 16/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 17/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 18/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 19/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 20/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 21/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 22/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 23/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 24/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 25/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 26/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 27/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 28/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 29/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 30/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 31/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 32/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 33/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 34/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 35/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 36/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 37/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 38/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 39/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 40/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 41/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 42/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 43/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 44/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 45/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 46/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 47/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 48/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 49/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 Epoch 50/50 1000/1000 [==============================] - 0s - loss: 7.8817 - acc: 0.5110 CPU times: user 3.9 s, sys: 612 ms, total: 4.52 s Wall time: 14.7 s
%%time
model = Sequential()
model.add(Dense(1,input_dim=784, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
data = np.random.random((1000,784))
labels = np.random.randint(2,size=(1000,1))
model.fit(data,labels,nb_epoch=10,batch_size=32)
Epoch 1/10 1000/1000 [==============================] - 0s - loss: 0.7277 - acc: 0.5130 Epoch 2/10 1000/1000 [==============================] - 0s - loss: 0.7125 - acc: 0.5300 Epoch 3/10 1000/1000 [==============================] - 0s - loss: 0.7040 - acc: 0.5030 Epoch 4/10 1000/1000 [==============================] - 0s - loss: 0.7012 - acc: 0.5450 Epoch 5/10 1000/1000 [==============================] - 0s - loss: 0.6945 - acc: 0.5410 Epoch 6/10 1000/1000 [==============================] - 0s - loss: 0.6837 - acc: 0.5570 Epoch 7/10 1000/1000 [==============================] - 0s - loss: 0.6777 - acc: 0.5680 Epoch 8/10 1000/1000 [==============================] - 0s - loss: 0.6756 - acc: 0.5870 Epoch 9/10 1000/1000 [==============================] - 0s - loss: 0.6645 - acc: 0.6030 Epoch 10/10 1000/1000 [==============================] - 0s - loss: 0.6560 - acc: 0.6110 CPU times: user 1.5 s, sys: 188 ms, total: 1.68 s Wall time: 11.3 s
%%time
model = Sequential()
model.add(Dense(1,input_dim=784, activation='hard_sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
data = np.random.random((1000,784))
labels = np.random.randint(2,size=(1000,1))
model.fit(data,labels,nb_epoch=10,batch_size=32)
Epoch 1/10 1000/1000 [==============================] - 0s - loss: 0.7120 - acc: 0.5130 Epoch 2/10 1000/1000 [==============================] - 0s - loss: 0.6932 - acc: 0.5540 Epoch 3/10 1000/1000 [==============================] - 0s - loss: 0.6892 - acc: 0.5550 Epoch 4/10 1000/1000 [==============================] - 0s - loss: 0.6840 - acc: 0.5790 Epoch 5/10 1000/1000 [==============================] - 0s - loss: 0.6763 - acc: 0.5750 Epoch 6/10 1000/1000 [==============================] - 0s - loss: 0.6795 - acc: 0.5670 Epoch 7/10 1000/1000 [==============================] - 0s - loss: 0.6643 - acc: 0.5960 Epoch 8/10 1000/1000 [==============================] - 0s - loss: 0.6600 - acc: 0.5970 Epoch 9/10 1000/1000 [==============================] - 0s - loss: 0.6530 - acc: 0.6080 Epoch 10/10 1000/1000 [==============================] - 0s - loss: 0.6490 - acc: 0.6230 CPU times: user 1.54 s, sys: 220 ms, total: 1.76 s Wall time: 13.7 s
%%time
from keras.regularizers import l1,l2,l1l2, activity_l2
model = Sequential()
model.add(Dense(1,input_dim=784, activation='sigmoid', W_regularizer=l2()))
# model.add(Dense(1,input_dim=784, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])
data = np.random.random((1000,784))
labels = np.random.randint(2,size=(1000,1))
model.fit(data,labels,nb_epoch=10,batch_size=2)
Epoch 1/10 1000/1000 [==============================] - 0s - loss: 0.8269 - acc: 0.4830 Epoch 2/10 1000/1000 [==============================] - 0s - loss: 0.7506 - acc: 0.5540 Epoch 3/10 1000/1000 [==============================] - 0s - loss: 0.6852 - acc: 0.6180 Epoch 4/10 1000/1000 [==============================] - 0s - loss: 0.6625 - acc: 0.6510 Epoch 5/10 1000/1000 [==============================] - 0s - loss: 0.6564 - acc: 0.6460 Epoch 6/10 1000/1000 [==============================] - 0s - loss: 0.6392 - acc: 0.6700 Epoch 7/10 1000/1000 [==============================] - 1s - loss: 0.6369 - acc: 0.6810 Epoch 8/10 1000/1000 [==============================] - 1s - loss: 0.6046 - acc: 0.7180 Epoch 9/10 1000/1000 [==============================] - 0s - loss: 0.6090 - acc: 0.7220 Epoch 10/10 1000/1000 [==============================] - 1s - loss: 0.6131 - acc: 0.7120 CPU times: user 9.64 s, sys: 1.76 s, total: 11.4 s Wall time: 26.9 s
data.shape
(1000, 784)
labels.shape
labels[:5]
array([[0], [0], [1], [1], [0]])
test = np.random.random(784).reshape(1,-1)
proba = model.predict_proba(test)
classes = model.predict_classes(test)
proba,classes
1/1 [==============================] - 0s 1/1 [==============================] - 0s
(array([[ 0.13410421]], dtype=float32), array([[0]], dtype=int32))