Python theano 模块，pp() 实例源码

我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用theano.pp()。

项目：third_person_im 作者：bstadie | 项目源码 | 文件源码

def cached_function(inputs, outputs):
    import theano
    with Message("Hashing theano fn"):
        if hasattr(outputs, '__len__'):
            hash_content = tuple(map(theano.pp, outputs))
        else:
            hash_content = theano.pp(outputs)
    cache_key = hex(hash(hash_content) & (2 ** 64 - 1))[:-1]
    cache_dir = Path('~/.hierctrl_cache')
    cache_dir = cache_dir.expanduser()
    cache_dir.mkdir_p()
    cache_file = cache_dir / ('%s.pkl' % cache_key)
    if cache_file.exists():
        with Message("unpickling"):
            with open(cache_file, "rb") as f:
                try:
                    return pickle.load(f)
                except Exception:
                    pass
    with Message("compiling"):
        fun = compile_function(inputs, outputs)
    with Message("picking"):
        with open(cache_file, "wb") as f:
            pickle.dump(fun, f, protocol=pickle.HIGHEST_PROTOCOL)
    return fun


# Immutable, lazily evaluated dict

项目：rllabplusplus 作者：shaneshixiang | 项目源码 | 文件源码

def cached_function(inputs, outputs):
    import theano
    with Message("Hashing theano fn"):
        if hasattr(outputs, '__len__'):
            hash_content = tuple(map(theano.pp, outputs))
        else:
            hash_content = theano.pp(outputs)
    cache_key = hex(hash(hash_content) & (2 ** 64 - 1))[:-1]
    cache_dir = Path('~/.hierctrl_cache')
    cache_dir = cache_dir.expanduser()
    cache_dir.mkdir_p()
    cache_file = cache_dir / ('%s.pkl' % cache_key)
    if cache_file.exists():
        with Message("unpickling"):
            with open(cache_file, "rb") as f:
                try:
                    return pickle.load(f)
                except Exception:
                    pass
    with Message("compiling"):
        fun = compile_function(inputs, outputs)
    with Message("picking"):
        with open(cache_file, "wb") as f:
            pickle.dump(fun, f, protocol=pickle.HIGHEST_PROTOCOL)
    return fun


# Immutable, lazily evaluated dict

项目：gail-driver 作者：sisl | 项目源码 | 文件源码

def cached_function(inputs, outputs):
    import theano
    with Message("Hashing theano fn"):
        if hasattr(outputs, '__len__'):
            hash_content = tuple(map(theano.pp, outputs))
        else:
            hash_content = theano.pp(outputs)
    cache_key = hex(hash(hash_content) & (2 ** 64 - 1))[:-1]
    cache_dir = Path('~/.hierctrl_cache')
    cache_dir = cache_dir.expanduser()
    cache_dir.mkdir_p()
    cache_file = cache_dir / ('%s.pkl' % cache_key)
    if cache_file.exists():
        with Message("unpickling"):
            with open(cache_file, "rb") as f:
                try:
                    return pickle.load(f)
                except Exception:
                    pass
    with Message("compiling"):
        fun = compile_function(inputs, outputs)
    with Message("picking"):
        with open(cache_file, "wb") as f:
            pickle.dump(fun, f, protocol=pickle.HIGHEST_PROTOCOL)
    return fun


# Immutable, lazily evaluated dict

项目：rllab 作者：rll | 项目源码 | 文件源码

def cached_function(inputs, outputs):
    import theano
    with Message("Hashing theano fn"):
        if hasattr(outputs, '__len__'):
            hash_content = tuple(map(theano.pp, outputs))
        else:
            hash_content = theano.pp(outputs)
    cache_key = hex(hash(hash_content) & (2 ** 64 - 1))[:-1]
    cache_dir = Path('~/.hierctrl_cache')
    cache_dir = cache_dir.expanduser()
    cache_dir.mkdir_p()
    cache_file = cache_dir / ('%s.pkl' % cache_key)
    if cache_file.exists():
        with Message("unpickling"):
            with open(cache_file, "rb") as f:
                try:
                    return pickle.load(f)
                except Exception:
                    pass
    with Message("compiling"):
        fun = compile_function(inputs, outputs)
    with Message("picking"):
        with open(cache_file, "wb") as f:
            pickle.dump(fun, f, protocol=pickle.HIGHEST_PROTOCOL)
    return fun


# Immutable, lazily evaluated dict

项目：maml_rl 作者：cbfinn | 项目源码 | 文件源码

def cached_function(inputs, outputs):
    import theano
    with Message("Hashing theano fn"):
        if hasattr(outputs, '__len__'):
            hash_content = tuple(map(theano.pp, outputs))
        else:
            hash_content = theano.pp(outputs)
    cache_key = hex(hash(hash_content) & (2 ** 64 - 1))[:-1]
    cache_dir = Path('~/.hierctrl_cache')
    cache_dir = cache_dir.expanduser()
    cache_dir.mkdir_p()
    cache_file = cache_dir / ('%s.pkl' % cache_key)
    if cache_file.exists():
        with Message("unpickling"):
            with open(cache_file, "rb") as f:
                try:
                    return pickle.load(f)
                except Exception:
                    pass
    with Message("compiling"):
        fun = compile_function(inputs, outputs)
    with Message("picking"):
        with open(cache_file, "wb") as f:
            pickle.dump(fun, f, protocol=pickle.HIGHEST_PROTOCOL)
    return fun


# Immutable, lazily evaluated dict

项目：rl 作者：wingedsheep | 项目源码 | 文件源码

def run(self, 
            epochs,  
            steps, 
            api_key,
            rollouts_per_epoch = 20,
            updateTargetNetwork = defaultRunSettings['updateTargetNetwork'], 
            explorationRate = defaultRunSettings['explorationRate'], 
            miniBatchSize = defaultRunSettings['miniBatchSize'], 
            learnStart = defaultRunSettings['learnStart'], 
            renderPerXEpochs = defaultRunSettings['renderPerXEpochs'], 
            shouldRender = defaultRunSettings['shouldRender'], 
            experimentId = defaultRunSettings['experimentId'], 
            force = defaultRunSettings['force'], 
            upload = defaultRunSettings['upload']):

        last100Scores = [0] * 100
        last100ScoresIndex = 0
        last100Filled = False

        stepCounter = 0

        if not experimentId == None:
            self.env.monitor.start('tmp/'+experimentId, force = force)

        for epoch in xrange(epochs):
            I = 1
            observation = self.env.reset();
            for t in xrange(steps):
                policyValues = self.runModel(self.policyModel, observation)
                action = self.selectActionByProbability(policyValues)

                newObservation, reward, done, info = self.env.step(action)

                cost, grads = self.get_cost_grads(self.policyModel);
                print (theano.pp(grads[1][0]));

                if done:
                    delta = reward + self.discountFactor * self.runModel(self.valueModel, newObservation) - self.runModel(self.valueModel, observation)
                else :
                    delta = reward - self.runModel(self.valueModel, observation) # because the value for new obs is 0

        self.env.monitor.close()
        if upload:
            gym.upload('/tmp/'+experimentId, api_key=api_key)