Python numpy 模块，argmax() 实例源码

我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.argmax()。

项目：gcForest 作者：pylablanche | 项目源码 | 文件源码

def _cascade_evaluation(self, X_test, y_test):
        """ Evaluate the accuracy of the cascade using X and y.

        :param X_test: np.array
            Array containing the test input samples.
            Must be of the same shape as training data.

        :param y_test: np.array
            Test target values.

        :return: float
            the cascade accuracy.
        """
        casc_pred_prob = np.mean(self.cascade_forest(X_test), axis=0)
        casc_pred = np.argmax(casc_pred_prob, axis=1)
        casc_accuracy = accuracy_score(y_true=y_test, y_pred=casc_pred)
        print('Layer validation accuracy = {}'.format(casc_accuracy))

        return casc_accuracy

项目：lang-reps 作者：chaitanyamalaviya | 项目源码 | 文件源码

def generate(self, src, sampled=False):
        dynet.renew_cg()

        embedding = self.embed_seq(src)
        encoding = self.encode_seq(embedding)[-1]

        w = dynet.parameter(self.decoder_w)
        b = dynet.parameter(self.decoder_b)

        s = self.dec_lstm.initial_state().add_input(encoding)

        out = []
        for _ in range(5*len(src)):
            out_vector = dynet.affine_transform([b, w, s.output()])
            probs = dynet.softmax(out_vector)
            selection = np.argmax(probs.value())
            out.append(self.tgt_vocab[selection])
            if out[-1].s == self.tgt_vocab.END_TOK: break
            embed_vector = self.tgt_lookup[selection]
            s = s.add_input(embed_vector)
        return out

项目：topically-driven-language-model 作者：jhlau | 项目源码 | 文件源码

def gen_sent_on_topic(idxvocab, vocabxid, start_symbol, end_symbol, cf):
    output = codecs.open(args.gen_sent_on_topic, "w", "utf-8")
    topics, entropy = tm.get_topics(sess, topn=topn)
    with tf.variable_scope("model", reuse=True, initializer=initializer):
        mgen = LM(is_training=False, vocab_size=len(idxvocab), batch_size=1, num_steps=1, config=cf, \
            reuse_conv_variables=True)

    for t in range(cf.topic_number):
        output.write("\n" + "="*100 + "\n")
        output.write("Topic " +  str(t) + ":\n")
        output.write(" ".join([ idxvocab[item] for item in topics[t] ]) + "\n\n")

        output.write("\nSentence generation (greedy; argmax):" + "\n")
        s = mgen.generate_on_topic(sess, t, vocabxid[start_symbol], 0, cf.lm_sent_len+10, vocabxid[end_symbol])
        output.write("[0] " + " ".join([ idxvocab[item] for item in s ]) + "\n")

        for temp in gen_temps:
            output.write("\nSentence generation (random; temperature = " + str(temp) + "):\n")
            for i in xrange(gen_num):
                s = mgen.generate_on_topic(sess, t, vocabxid[start_symbol], temp, cf.lm_sent_len+10, \
                    vocabxid[end_symbol])
                output.write("[" + str(i) + "] " +  " ".join([ idxvocab[item] for item in s ]) + "\n")

项目：onto-lstm 作者：pdasigi | 项目源码 | 文件源码

def write_predictions(self, inputs):
        '''
        Outputs predictions in a file named <model_name_prefix>.predictions.
        '''
        predictions = numpy.argmax(self.model.predict(inputs), axis=1)
        test_output_file = open("%s.predictions" % self.model_name_prefix, "w")
        for input_indices, prediction in zip(inputs, predictions):
            # The predictions are indices of words in padded sentences. We need to readjust them.
            padding_length = 0
            for index in input_indices:
                if numpy.all(index == 0):
                    padding_length += 1
                else:
                    break
            prediction = prediction - padding_length + 1  # +1 because the indices start at 1.
            print >>test_output_file, prediction

项目：cellranger 作者：10XGenomics | 项目源码 | 文件源码

def _infer_multiplets_from_observed(n_obs_multiplets, n_cells0, n_cells1):
        """ Given a number of observed multiplets and cell counts for two transcriptomes,
        infer the total number of multiplets (observed + unobserved) """

        if n_cells0 == 0 or n_cells1 == 0:
            return 0

        # Prior probability of a doublet given counts for each cell type (ignore N_cells > 2)
        p_obs_multiplet = 2*(float(n_cells0)/float(n_cells0+n_cells1))*(float(n_cells1)/float(n_cells0+n_cells1))

        # Brute force MLE of binomial n
        n_mle = 0
        if n_obs_multiplets > 0:
            likelihood = scipy.stats.binom.pmf(n_obs_multiplets, xrange(0, n_cells0 + n_cells1), p_obs_multiplet)
            n_mle = np.argmax(likelihood)
        return n_mle

项目：dl-classification 作者：matthieuo | 项目源码 | 文件源码

def has_tomatoes(self, im_path):
        # load the image
        im = Image.open(im_path)
        im = np.asarray(im, dtype=np.float32)
        im = self.prepare_image(im)

        # launch an inference with the image
        pred = self.sess.run(
            self.output_logits, feed_dict={
                self.img_feed: im.eval(
                    session=self.sess)})

        if np.argmax(pred) == 0:
            print("NOT a tomato ! (confidence : ", pred[0, 0], "%)")
        else:
            print("We have a tomato ! (confidence : ", pred[0, 1], "%)")

项目：snake_game 作者：wing3s | 项目源码 | 文件源码

def play(self, nb_rounds):
        img_saver = save_image()
        img_saver.next()

        game_cnt = it.count(1)
        for i in xrange(nb_rounds):
            game = self.game(width=self.width, height=self.height)
            screen, _ = game.next()
            img_saver.send(screen)
            frame_cnt = it.count()
            try:
                state = np.asarray([screen] * self.nb_frames)
                while True:
                    frame_cnt.next()
                    act_idx = np.argmax(
                        self.model.predict(state[np.newaxis]), axis=-1)[0]
                    screen, _ = game.send(self.actions[act_idx])
                    state = np.roll(state, 1, axis=0)
                    state[0] = screen
                    img_saver.send(screen)
            except StopIteration:
                print 'Saved %4i frames for game %3i' % (
                    frame_cnt.next(), game_cnt.next())
        img_saver.close()

项目：aapm_thoracic_challenge 作者：xf4j | 项目源码 | 文件源码

def test(self, input_path, output_path):
        if not self.load()[0]:
            raise Exception("No model is found, please train first")

        mean, std = self.sess.run([self.mean, self.std])

        images = np.empty((1, self.im_size[0], self.im_size[1], self.im_size[2], 1), dtype=np.float32)
        #labels = np.empty((1, self.im_size[0], self.im_size[1], self.im_size[2], self.nclass), dtype=np.float32)
        for f in input_path:
            images[0, ..., 0], read_info = read_testing_inputs(f, self.roi[0], self.im_size, output_path)
            probs = self.sess.run(self.probs, feed_dict = { self.images: (images - mean) / std,
                                                            self.is_training: True,
                                                            self.keep_prob: 1 })
            #print(self.roi[1] + os.path.basename(f) + ":" + str(dice))
            output_file = os.path.join(output_path, self.roi[1] + '_' + os.path.basename(f))
            f_h5 = h5py.File(output_file, 'w')
            if self.roi[0] < 0:
                f_h5['predictions'] = restore_labels(np.argmax(probs[0], 3), self.roi[0], read_info)
            else:
                f_h5['probs'] = restore_labels(probs[0, ..., 1], self.roi[0], read_info)
            f_h5.close()

项目：lung-cancer-detector 作者：YichenGong | 项目源码 | 文件源码

def __init__(self, channels=3, n_class=2, cost="cross_entropy", cost_kwargs={}, **kwargs):
        tf.reset_default_graph()

        self.n_class = n_class
        self.summaries = kwargs.get("summaries", True)

        self.x = tf.placeholder("float", shape=[None, None, None, channels])
        self.y = tf.placeholder("float", shape=[None, None, None, n_class])
        self.keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)

        logits, self.variables, self.offset = create_conv_net(self.x, self.keep_prob, channels, n_class, **kwargs)

        self.cost = self._get_cost(logits, cost, cost_kwargs)

        self.gradients_node = tf.gradients(self.cost, self.variables)

        self.cross_entropy = tf.reduce_mean(cross_entropy(tf.reshape(self.y, [-1, n_class]),
                                                          tf.reshape(pixel_wise_softmax_2(logits), [-1, n_class])))

        self.predicter = pixel_wise_softmax_2(logits)
        self.correct_pred = tf.equal(tf.argmax(self.predicter, 3), tf.argmax(self.y, 3))
        self.accuracy = tf.reduce_mean(tf.cast(self.correct_pred, tf.float32))

项目：brain_segmentation 作者：Ryo-Ito | 项目源码 | 文件源码

def validate(model):
    dice_coefs = []
    for image_path, label_path in zip(df_val["image"], df_val["label"]):
        image = load_nifti(image_path)
        label = load_nifti(label_path)
        centers = [[], [], []]
        for img_len, len_out, center, n_tile in zip(image.shape, args.output_shape, centers, args.n_tiles):
            assert img_len < len_out * n_tile, "{} must be smaller than {} x {}".format(img_len, len_out, n_tile)
            stride = int((img_len - len_out) / (n_tile - 1))
            center.append(len_out / 2)
            for i in range(n_tile - 2):
                center.append(center[-1] + stride)
            center.append(img_len - len_out / 2)
        output = np.zeros((dataset["n_classes"],) + image.shape[:-1])
        for x, y, z in itertools.product(*centers):
            patch = crop_patch(image, [x, y, z], args.input_shape)
            patch = np.expand_dims(patch, 0)
            patch = xp.asarray(patch)
            slices_out = [slice(center - len_out / 2, center + len_out / 2) for len_out, center in zip(args.output_shape, [x, y, z])]
            slices_in = [slice((len_in - len_out) / 2, len_in - (len_in - len_out) / 2) for len_out, len_in, in zip(args.output_shape, args.input_shape)]
            output[slice(None), slices_out[0], slices_out[1], slices_out[2]] += chainer.cuda.to_cpu(model(patch).data[0, slice(None), slices_in[0], slices_in[1], slices_in[2]])
        y = np.argmax(output, axis=0).astype(np.int32)
        dice_coefs.append(dice_coefficients(y, label, labels=range(dataset["n_classes"])))
    dice_coefs = np.array(dice_coefs)
    return np.mean(dice_coefs, axis=0)

项目：stcad 作者：feschmidt | 项目源码 | 文件源码

def correct_for_multipol(pol):
    """
    Inputs are:
        pol, Suspected Multipolygon
    Takes the main polygon of a multipolygon.

    Typically used to solve the problem of non-overlapping polygons being substracted.

    """
    pol_type = pol.geom_type
    if pol_type == 'MultiPolygon':
        area = np.zeros(len(pol.geoms))
        for k, p in enumerate(pol.geoms):
            area[k] = p.area
        max_area_id = np.argmax(area)
        pol = pol.geoms[max_area_id]
    return pol