recipes = [name for i in range(0,batchSize)]
div = np.linspace(0.2,1.,batchSize)
startLen = len(recipes[0])
#create a batch of recipes
ind = 0
epochLoss = []
for cInd in range(400):
# if (cInd+maxlen) < startLen:
# recs = [r[cInd:cInd+maxlen] for r in recipes]
# else:
# recs = [r[-maxlen:] for r in recipes]
#print(maxlen, maxWord,len(mh.char_indices),len(mh.word_indices))
Xchar,Xword,Xcon,dummy = helper.getCharAndWordNoState(recipes,contextVec,maxlen,maxWord,mh.char_indices,mh.word_indices,step=1,predict=True)
newLength = (Xchar.shape[0])/4
inds = [(newLength*(divind+1))-1 for divind in range(0,batchSize)]
#helper.checkExampleWords(Xword[inds[1]],mh.vocab)
Xchar = Xchar[inds]
Xword = Xword[inds]
Xcon = Xcon[inds]
preds = model.predict_on_batch([Xchar,Xword,Xcon])[0]
for d,pred in enumerate(preds):
#print(d,pred)
next_index = helper.sample(pred, div[d])
model.add(LSTM(512, return_sequences=False))
model.add(Dropout(.2))
model.add(Dense(mh.numChars))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
#create a batch of recipes
ind = 0
print("begin training")
while True:
if ind + batchSize > len(recipes):
ind = 0
np.random.shuffle(recipes)
Xcontext = contextVecs[ind:ind+batchSize]
recs = recipes[ind:ind+batchSize]
recs = helper.shuffleIngredients(recs)
Xcharacter, Xword, Xcontext, y = helper.getCharAndWordNoState(recs,Xcontext,maxlen,maxWord,mh.char_indices,mh.word_indices,step=step)
loss = model.fit([Xcharacter, Xword, Xcontext], y,nb_epoch=1,batch_size=batchSize)
ind = ind+batchSize
if (ind / batchSize) % 10 == 0:
print()
jsonstring = model.to_json()
with open("../models/recipeRNN3noState.json",'wb') as f:
f.write(jsonstring)
model.save_weights("../models/recipeRNN3noState.h5",overwrite=True)
model.add(Dense(mh.numChars))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
#create a batch of recipes
ind = 0
print("begin training")
while True:
if ind + batchSize > len(recipes):
print("new epoch")
ind = 0
recipes, contextVecs = helper.sameShuffle(recipes,contextVecs)
contextVecs = np.array(contextVecs)
Xcontext = contextVecs[ind:ind+batchSize]
recs = recipes[ind:ind+batchSize]
recs = helper.shuffleIngredients(recs)
Xcharacter, Xword, Xcontext, y = helper.getCharAndWordNoState(recs,Xcontext,maxlen,maxWord,mh.char_indices,mh.word_indices,step=step)
loss = model.fit([Xcharacter, Xword, Xcontext], y,nb_epoch=1,batch_size=batchSize)
ind = ind+batchSize
if (ind / batchSize) % 10 == 0:
print(ind)
jsonstring = model.to_json()
with open("../models/recipeRNN3noState.json",'wb') as f:
f.write(jsonstring)
model.save_weights("../models/recipeRNN3noState.h5",overwrite=True)
