# In[51]:
WEIGHTS_PATH = "/home/rishabh/siamese/keras-oneshot/weights"
# siamese_net.load_weights(WEIGHTS_PATH)
# In[ ]:
history = siamese_net.fit_generator(
datagen.next_train(),
steps_per_epoch=STEPS_PER_EPOCH,
epochs=500,
validation_data = datagen.next_val(),
validation_steps = VALIDATION_STEPS,
callbacks = [scheduler, reduce_lr, early_stopping, loss_history, checkpointer])
# In[30]:
#get_ipython().magic(u'matplotlib inline')
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
f.set_figheight(8)
f.set_figwidth(14)
ax1.plot(history.history['loss'])
ax1.set_title('model loss')
ax1.set_ylabel('loss')
from keras.optimizers import Adam
adam = Adam(1e-3)
triplet_net.compile(loss=triplet_loss, optimizer=adam)
triplet_net.load_weights(INIT_WEIGHTS)
# In[36]:
# triplet_net.load_weights(CHECKPOINTED_WEIGHTS)
history = triplet_net.fit_generator(
datagen.next_train(),
steps_per_epoch=STEPS_PER_EPOCH,
epochs=500,
validation_data=datagen.next_val(),
validation_steps=VALIDATION_STEPS,
callbacks = [reduce_lr, loss_history, checkpointer, early_stopping])
# In[ ]:
triplet_net.load_weights(CHECKPOINTED_WEIGHTS)
# In[ ]:
# triplet_net.load_weights(CHECKPOINTED_WEIGHTS)
history = triplet_net.evaluate_generator(
