def test_image_data_generator_training(): np.random.seed(1337) img_gen = ImageDataGenerator(rescale=1.) # Dummy ImageDataGenerator input_shape = (16, 16, 3) (x_train, y_train), (x_test, y_test) = get_test_data(num_train=500, num_test=200, input_shape=input_shape, classification=True, num_classes=4) y_train = to_categorical(y_train) y_test = to_categorical(y_test) model = Sequential([ layers.Conv2D(filters=8, kernel_size=3, activation='relu', input_shape=input_shape), layers.MaxPooling2D(pool_size=2), layers.Conv2D(filters=4, kernel_size=(3, 3), activation='relu', padding='same'), layers.GlobalAveragePooling2D(), layers.Dense(y_test.shape[-1], activation='softmax') ]) model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) history = model.fit_generator(img_gen.flow(x_train, y_train, batch_size=16), epochs=10, validation_data=img_gen.flow(x_test, y_test, batch_size=16), verbose=0) assert history.history['val_acc'][-1] > 0.75 model.evaluate_generator(img_gen.flow(x_train, y_train, batch_size=16))
def train_CAE(): encoder = containers.Sequential() encoder.add(Permute((3,1,2),input_shape=(h,w,ch))) # reorder input to ch, h, w (no sample axis) encoder.add(GaussianNoise(0.05)) # corrupt inputs slightly encoder.add(Convolution2D(16,3,3,init='glorot_uniform',border_mode='same')) encoder.add(MaxPooling2D((2,2))) encoder.add(Activation('tanh')) encoder.add(Convolution2D(32,3,3,init='glorot_uniform',border_mode='same')) encoder.add(MaxPooling2D((2,2))) encoder.add(Activation('tanh')) decoder = containers.Sequential() decoder.add(UpSampling2D((2,2),input_shape=(32,32,32))) decoder.add(Convolution2D(3,3,3,init='glorot_uniform',border_mode='same')) decoder.add(Activation('tanh')) decoder.add(UpSampling2D((2,2),input_shape=(16,64,64))) decoder.add(Convolution2D(3,3,3,init='glorot_uniform',border_mode='same')) decoder.add(Activation('tanh')) decoder.add(Permute((2,3,1))) autoencoder = AutoEncoder(encoder,decoder) model = Sequential() model.add(autoencoder) model.compile(optimizer='rmsprop', loss='mae') # if shapes don't match, check the output_shape of encoder/decoder genr = image_generator(biz_id_train['photo_id'], batch_size) model.fit_generator(genr, samples_per_epoch=len(biz_id_train), nb_epoch=10)
def train(dataReader, oneHot, oneHotAveraged, contextHashes): n = (Epochs + 1) * SamplesPerEpoch # TODO + 1 should not be needed tokeniser = Tokenizer(nb_words=MaxWords) tokeniser.fit_on_texts((row[0] for row in dataReader.trainingData(n))) # `word_index` maps each word to its unique index dictionarySize = len(tokeniser.word_index) + 1 oneHotDimension = (1 if oneHotAveraged else SequenceLength) * dictionarySize if oneHot else 0 contextHashesDimension = dictionarySize * 2 if contextHashes else 0 model = Sequential() model.add(Dense(EmbeddingDim, input_dim=(oneHotDimension + contextHashesDimension))) model.add(Dense(Labels, activation='softmax')) model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) trainingGenerator = mapGenerator(dataReader.trainingData(n), tokeniser, dictionarySize, oneHot, oneHotAveraged, contextHashes) validationGenerator = mapGenerator(dataReader.validationData(n), tokeniser, dictionarySize, oneHot, oneHotAveraged, contextHashes) model.fit_generator(trainingGenerator, nb_epoch=Epochs, samples_per_epoch=SamplesPerEpoch, validation_data=validationGenerator, nb_val_samples=SamplesPerEpoch) model2 = Sequential() model2.add(Dense(EmbeddingDim, input_dim=(oneHotDimension + contextHashesDimension), weights=model.layers[0].get_weights())) return model, model2, tokeniser, dictionarySize
def test_multiprocessing_fit_error(): batch_size = 10 good_batches = 3 def custom_generator(): """Raises an exception after a few good batches""" for i in range(good_batches): yield (np.random.randint(batch_size, 256, (50, 2)), np.random.randint(batch_size, 2, 50)) raise RuntimeError model = Sequential() model.add(Dense(1, input_shape=(2, ))) model.compile(loss='mse', optimizer='adadelta') samples = batch_size * (good_batches + 1) with pytest.raises(StopIteration): model.fit_generator( custom_generator(), samples, 1, workers=4, use_multiprocessing=True, ) with pytest.raises(StopIteration): model.fit_generator( custom_generator(), samples, 1, use_multiprocessing=False, )
def main(): ext = extension_from_parameters() out_dim = 1 loss = 'mse' metrics = None #metrics = ['accuracy'] if CATEGORICAL else None datagen = RegressionDataGenerator() train_gen = datagen.flow(batch_size=BATCH_SIZE) val_gen = datagen.flow(val=True, batch_size=BATCH_SIZE) val_gen2 = datagen.flow(val=True, batch_size=BATCH_SIZE) model = Sequential() for layer in LAYERS: if layer: model.add(Dense(layer, input_dim=datagen.input_dim, activation=ACTIVATION)) if DROP: model.add(Dropout(DROP)) model.add(Dense(out_dim)) model.summary() model.compile(loss=loss, optimizer='rmsprop', metrics=metrics) train_samples = int(datagen.n_train/BATCH_SIZE) * BATCH_SIZE val_samples = int(datagen.n_val/BATCH_SIZE) * BATCH_SIZE history = BestLossHistory(val_gen2, val_samples, ext) checkpointer = ModelCheckpoint(filepath='model'+ext+'.h5', save_best_only=True) model.fit_generator(train_gen, train_samples, nb_epoch = NB_EPOCH, validation_data = val_gen, nb_val_samples = val_samples, callbacks=[history, checkpointer])
class CNN(object): def __init__(self): self.model = Sequential([ Conv2D(50, (3, 3), input_shape=(28, 28, 1), padding='same', activation='relu'), Conv2D(50, (3, 3), input_shape=(28, 28, 1), padding='same', activation='relu'), MaxPool2D(pool_size=(4, 4), strides=(3, 3), padding='same'), Conv2D(32, (3, 3), padding='same', activation='relu' ), Conv2D(32, (3, 3), padding='same', activation='relu' ), MaxPool2D(pool_size=(7, 7), strides=(3, 3), padding='same'), Flatten(), Dropout(0.5), Dense(64, activation='relu'), Dense(10, activation='softmax') ]) self.model.compile(loss=categorical_crossentropy, optimizer=Adam(), metrics=['accuracy']) def train(self): mnist = input_data.read_data_sets("../MNIST_data/", one_hot=True) train_datagen = ImageDataGenerator(rotation_range=20, width_shift_range=0.2, height_shift_range=0.2) train_datagen.fit(mnist.train.images.reshape(-1, 28, 28, 1)) x_test, y_test = mnist.test.images.reshape(-1, 28, 28, 1), mnist.test.labels self.model.fit_generator(train_datagen.flow(mnist.train.images.reshape(-1, 28, 28, 1), mnist.train.labels), #batch_size=128, epochs=20, verbose=1, validation_data=(x_test, y_test), callbacks=[TrainValTensorBoard(log_dir='./logs/cnn4', histogram_freq=1, write_grads=True)]) score = self.model.evaluate(x_test, y_test, verbose=0) print('Loss', score[0], 'acc', score[1])
def test_multiprocessing_fit_error(): batch_size = 32 good_batches = 5 def myGenerator(): """Raises an exception after a few good batches""" for i in range(good_batches): yield (np.random.randint(batch_size, 256, (500, 2)), np.random.randint(batch_size, 2, 500)) raise RuntimeError model = Sequential() model.add(Dense(1, input_shape=(2, ))) model.compile(loss='mse', optimizer='adadelta') samples = batch_size * (good_batches + 1) with pytest.raises(Exception): model.fit_generator( myGenerator(), samples, 1, nb_worker=4, pickle_safe=True, ) with pytest.raises(Exception): model.fit_generator( myGenerator(), samples, 1, pickle_safe=False, )
class MLP(BaseEstimator): def __init__(self, verbose=0, model=None, final_activation='sigmoid'): self.verbose = verbose self.model = model self.final_activation = final_activation def fit(self, X, y): if not self.model: self.model = Sequential() self.model.add(Dense(1000, input_dim=X.shape[1])) self.model.add(Activation('relu')) self.model.add(Dropout(0.5)) self.model.add(Dense(y.shape[1])) self.model.add(Activation(self.final_activation)) self.model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.01)) self.model.fit_generator(generator=_batch_generator(X, y, 256, True), samples_per_epoch=X.shape[0], nb_epoch=20, verbose=self.verbose) def predict(self, X): pred = self.predict_proba(X) return sparse.csr_matrix(pred > 0.2) def predict_proba(self, X): pred = self.model.predict_generator(generator=_batch_generatorp(X, 512), val_samples=X.shape[0]) return pred
def test_CallbackValData(): np.random.seed(1337) (X_train, y_train), (X_test, y_test) = get_data_callbacks() y_test = np_utils.to_categorical(y_test) y_train = np_utils.to_categorical(y_train) model = Sequential() model.add(Dense(num_hidden, input_dim=input_dim, activation='relu')) model.add(Dense(num_classes, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) cbk = callbacks.LambdaCallback(on_train_end=lambda x: 1) model.fit(X_train, y_train, batch_size=batch_size, validation_data=(X_test, y_test), callbacks=[cbk], epochs=1) cbk2 = callbacks.LambdaCallback(on_train_end=lambda x: 1) train_generator = data_generator(X_train, y_train, batch_size) model.fit_generator(train_generator, len(X_train), epochs=1, validation_data=(X_test, y_test), callbacks=[cbk2]) # callback validation data should always have x, y, and sample weights assert len(cbk.validation_data) == len(cbk2.validation_data) == 3 assert cbk.validation_data[0] is cbk2.validation_data[0] assert cbk.validation_data[1] is cbk2.validation_data[1] assert cbk.validation_data[2].shape == cbk2.validation_data[2].shape
def CNN(trainDir, validationDir, classNum): model = Sequential() model.add(Convolution2D(4, 3, 3, input_shape=(img_width, img_height, 1))) model.add(Activation('relu')) model.add(Convolution2D(4, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) # layer model.add(Convolution2D(8, 3, 3)) model.add(Activation('relu')) model.add(Convolution2D(8, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Convolution2D(16, 3, 3)) # model.add(Activation('relu')) # model.add(MaxPooling2D(pool_size=(2, 2))) # layer model.add(Flatten()) model.add(Dense(64)) model.add(Activation('relu')) # model.add(Dropout(0.5)) model.add(Dense(16)) model.add(Activation('relu')) model.add(Dropout(0.6)) model.add(Dense(classNum)) model.add(Activation('softmax')) # test model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # this is the augmentation configuration we will use for training train_datagen = ImageDataGenerator( rescale=1./255, shear_range=0.2, zca_whitening=True, zoom_range=0.2, horizontal_flip=False) # this is the augmentation configuration we will use for testing: # only rescaling test_datagen = ImageDataGenerator(rescale=1./255, zca_whitening=True) train_generator = train_datagen.flow_from_directory( trainDir, target_size=(img_width, img_height), batch_size=32, color_mode='grayscale', class_mode='categorical') validation_generator = test_datagen.flow_from_directory( validationDir, target_size=(img_width, img_height), batch_size=32, color_mode='grayscale', class_mode='categorical') model.fit_generator( train_generator, samples_per_epoch=nb_train_samples, nb_epoch=nb_epoch, validation_data=validation_generator, nb_val_samples=nb_validation_samples) return model
def try_params( n_iterations, params, data=None, datamode='memory'): print "iterations:", n_iterations print_params( params ) batchsize = 100 if datamode == 'memory': X_train, Y_train = data['train'] X_valid, Y_valid = data['valid'] inputshape = X_train.shape[1:] else: train_generator = data['train']['gen_func'](batchsize, data['train']['path']) valid_generator = data['valid']['gen_func'](batchsize, data['valid']['path']) train_epoch_step = data['train']['n_sample'] / batchsize valid_epoch_step = data['valid']['n_sample'] / batchsize inputshape = data['train']['gen_func'](batchsize, data['train']['path']).next()[0].shape[1:] model = Sequential() model.add(Conv2D(128, (1, 24), padding='same', input_shape=inputshape, activation='relu')) model.add(GlobalMaxPooling2D()) model.add(Dense(32,activation='relu')) model.add(Dropout(params['DROPOUT'])) model.add(Dense(2)) model.add(Activation('softmax')) optim = Adadelta myoptimizer = optim(epsilon=params['DELTA'], rho=params['MOMENT']) mylossfunc = 'categorical_crossentropy' model.compile(loss=mylossfunc, optimizer=myoptimizer,metrics=['accuracy']) early_stopping = EarlyStopping( monitor = 'val_loss', patience = 3, verbose = 0 ) if datamode == 'memory': model.fit( X_train, Y_train, batch_size=batchsize, epochs=int( round( n_iterations )), validation_data=(X_valid, Y_valid), callbacks = [ early_stopping ]) score, acc = model.evaluate(X_valid,Y_valid) else: model.fit_generator( train_generator, steps_per_epoch=train_epoch_step, epochs=int( round( n_iterations )), validation_data=valid_generator, validation_steps=valid_epoch_step, callbacks = [ early_stopping ]) score, acc = model.evaluate_generator(valid_generator, steps=valid_epoch_step) return { 'loss': score, 'model': (model.to_json(), optim, myoptimizer.get_config(), mylossfunc) }
def lm(): maxlen=10 cfig = getattr(config, 'get_config_morph')('cs') batch_size, nb_epoch = cfig['batch_size'], 200 X_train, y_train = getTextFile(cfig['train_file'], cfig['train_dic'], cfig) X_train = sequence.pad_sequences(X_train, maxlen=maxlen, padding='post') y_train = sequence.pad_sequences(y_train, maxlen=maxlen, padding='post') #X_train, y_train = X_train[:10050], y_train[:10050] print X_train.shape, y_train.shape #X_test, y_test = getTextFile(cfig['test_file'], cfig['train_dic'], cfig) #X_test = sequence.pad_sequences(X_test, maxlen=10, padding='post') #y_test = sequence.pad_sequences(y_test, maxlen=10, padding='post') ''' y_train_tensor3 = np.zeros((len(X_train), maxlen, cfig['vocab_size']), dtype=np.bool) i, t = 0, 0 for sentence in y_train: t = 0 for v in sentence: y_train_tensor3[i][t][v] = True t += 1 i += 1 k = 0 for i , j in generate_data(X_train, y_train, 200, cfig['vocab_size']): print i.shape , j.shape if k > 20: break k += 1 exit(0) ''' print 'Build model...' model = Sequential() model.add(Embedding(cfig['vocab_size'], 128, dropout=0.2)) model.add(LSTM(128, return_sequences=True)) #- original model.add(TimeDistributedDense(cfig['vocab_size'])) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='rmsprop') model.summary() print 'Train...' model.fit_generator(generate_data(X_train, y_train, batch_size, cfig['vocab_size']), #samples_per_epoch=len(X_train)/batch_size, samples_per_epoch=1000, nb_epoch=nb_epoch) #model.fit(X_train, y_train_tensor3) exit(0) cnt = 0 for i , j in generate_data(X_train, y_train, 200, cfig['vocab_size']): #model.train_on_batch(i, j) history= model.fit(i, j, batch_size=10, nb_epoch=1,verbose=0) if cnt >= 3: break cnt += 1
def train_model(genre, dir_model, MP): sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) #check gpu is being used batch_size = MP['bs'] lstm_size = MP['lstm_size'] seq_length = MP['seq_length'] drop = MP['dropout'] lr = MP['lr'] epochs = MP['epochs'] text_to_int, int_to_text, n_chars = np.load('playlists/%s/ancillary_char.npy'%genre) vocab_size = len(text_to_int) X = np.load('playlists/%s/X_sl%d_char.npy'%(genre, seq_length)) y = np.load('playlists/%s/y_sl%d_char.npy'%(genre, seq_length)) # randomly shuffle samples before test/valid split np.random.seed(40) ran = [i for i in range(len(X))] np.random.shuffle(ran) X_train, X_valid, y_train, y_valid = train_test_split(X[ran], y[ran], test_size=0.2, random_state=42) try: model = load_model(dir_model) print("successfully loaded previous model, continuing to train") except: print("generating new model") model = Sequential() model.add(GRU(lstm_size, dropout=drop, recurrent_dropout=drop, return_sequences=True, input_shape=(seq_length, vocab_size))) for i in range(MP['n_layers'] - 1): model.add(GRU(lstm_size, dropout=drop, recurrent_dropout=drop, return_sequences=True)) model.add(TimeDistributed(Dense(vocab_size, activation='softmax'))) #output shape=(bs, sl, vocab) decay = 0.5*lr/epochs optimizer = Adam(lr=lr, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=decay, clipvalue=1) #optimizer = RMSprop(lr=lr, decay=decay) model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['categorical_accuracy']) print(model.summary()) # callbacks checkpoint = ModelCheckpoint(dir_model, monitor='loss', save_best_only=True, mode='min') #earlystop = EarlyStopping(monitor='val_loss', min_delta=0.01, patience=3) callbacks_list = [checkpoint] # train model.fit_generator(one_hot_gen(X_train, y_train, vocab_size, seq_length, batch_size), steps_per_epoch=len(X_train)/batch_size, epochs=epochs, callbacks=callbacks_list, validation_data=one_hot_gen(X_valid, y_valid, vocab_size, seq_length, batch_size), validation_steps=len(X_valid)/batch_size) model.save(dir_model)
def test_sequential_fit_generator(): (X_train, y_train), (X_test, y_test) = _get_test_data() def data_generator(train): if train: max_batch_index = len(X_train) // batch_size else: max_batch_index = len(X_test) // batch_size i = 0 while 1: if train: yield (X_train[i * batch_size: (i + 1) * batch_size], y_train[i * batch_size: (i + 1) * batch_size]) else: yield (X_test[i * batch_size: (i + 1) * batch_size], y_test[i * batch_size: (i + 1) * batch_size]) i += 1 i = i % max_batch_index model = Sequential() model.add(Dense(nb_hidden, input_shape=(input_dim,))) model.add(Activation('relu')) model.add(Dense(nb_class)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='rmsprop') model.fit_generator(data_generator(True), len(X_train), nb_epoch, show_accuracy=False) model.fit_generator(data_generator(True), len(X_train), nb_epoch, show_accuracy=True) model.fit_generator(data_generator(True), len(X_train), nb_epoch, show_accuracy=False, validation_data=(X_test, y_test)) model.fit_generator(data_generator(True), len(X_train), nb_epoch, show_accuracy=True, validation_data=(X_test, y_test)) loss = model.evaluate(X_train, y_train, verbose=0) assert(loss < 0.9)
def test_sequential_fit_generator(): (x_train, y_train), (x_test, y_test) = _get_test_data() def data_generator(train): if train: max_batch_index = len(x_train) // batch_size else: max_batch_index = len(x_test) // batch_size i = 0 while 1: if train: yield (x_train[i * batch_size: (i + 1) * batch_size], y_train[i * batch_size: (i + 1) * batch_size]) else: yield (x_test[i * batch_size: (i + 1) * batch_size], y_test[i * batch_size: (i + 1) * batch_size]) i += 1 i = i % max_batch_index model = Sequential() model.add(Dense(num_hidden, input_shape=(input_dim,))) model.add(Activation('relu')) model.add(Dense(num_class)) model.pop() model.add(Dense(num_class)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='rmsprop') model.fit_generator(data_generator(True), 5, epochs) model.fit_generator(data_generator(True), 5, epochs, validation_data=(x_test, y_test)) model.fit_generator(data_generator(True), 5, epochs, validation_data=data_generator(False), validation_steps=3) model.fit_generator(data_generator(True), 5, epochs, max_queue_size=2) model.evaluate(x_train, y_train)
def model(datagen, X_train, Y_train, X_test, Y_test): batch_size = 32 nb_epoch = 200 # input image dimensions img_rows, img_cols = 32, 32 # the CIFAR10 images are RGB img_channels = 3 model = Sequential() model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=(img_channels, img_rows, img_cols))) model.add(Activation('relu')) model.add(Convolution2D(32, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout({{uniform(0, 1)}})) model.add(Convolution2D(64, 3, 3, border_mode='same')) model.add(Activation('relu')) model.add(Convolution2D(64, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout({{uniform(0, 1)}})) model.add(Flatten()) model.add(Dense(512)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(nb_classes)) model.add(Activation('softmax')) # let's train the model using SGD + momentum (how original). sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) # fit the model on the batches generated by datagen.flow() model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size), samples_per_epoch=X_train.shape[0], nb_epoch=nb_epoch, validation_data=(X_test, Y_test)) score, acc = model.evaluate(X_test, Y_test, verbose=0) return {'loss': -acc, 'status': STATUS_OK, 'model': model}
def test_multiprocessing_training_fromfile(): reached_end = False arr_data = np.random.randint(0,256, (500, 200)) arr_labels = np.random.randint(0, 2, 500) np.savez("data.npz", **{"data": arr_data, "labels": arr_labels}) def myGenerator(): batch_size = 32 n_samples = 500 arr = np.load("data.npz") while True: batch_index = np.random.randint(0, n_samples - batch_size) start = batch_index end = start + batch_size X = arr["data"][start: end] y = arr["labels"][start: end] yield X, y # Build a NN model = Sequential() model.add(Dense(10, input_shape=(200, ))) model.add(Activation('relu')) model.add(Dense(1)) model.add(Activation('linear')) model.compile(loss='mse', optimizer='adadelta') model.fit_generator(myGenerator(), samples_per_epoch=320, nb_epoch=1, verbose=1, max_q_size=10, nb_worker=2, pickle_safe=True) model.fit_generator(myGenerator(), samples_per_epoch=320, nb_epoch=1, verbose=1, max_q_size=10, pickle_safe=False) reached_end = True assert reached_end
def test_TerminateOnNaN(): np.random.seed(1337) (X_train, y_train), (X_test, y_test) = get_data_callbacks() y_test = np_utils.to_categorical(y_test) y_train = np_utils.to_categorical(y_train) cbks = [callbacks.TerminateOnNaN()] model = Sequential() initializer = initializers.Constant(value=1e5) for _ in range(5): model.add(Dense(num_hidden, input_dim=input_dim, activation='relu', kernel_initializer=initializer)) model.add(Dense(num_classes, activation='linear')) model.compile(loss='mean_squared_error', optimizer='rmsprop') # case 1 fit history = model.fit(X_train, y_train, batch_size=batch_size, validation_data=(X_test, y_test), callbacks=cbks, epochs=20) loss = history.history['loss'] assert len(loss) == 1 assert loss[0] == np.inf history = model.fit_generator(data_generator(X_train, y_train, batch_size), len(X_train), validation_data=(X_test, y_test), callbacks=cbks, epochs=20) loss = history.history['loss'] assert len(loss) == 1 assert loss[0] == np.inf or np.isnan(loss[0])
def Convolution(test, df, flip_indices): img_rows, img_cols = 96, 96 # images are 96x96 pixels img_channels = 1 # images are grey scale - if RGB use img_channels = 3 nb_filter = 32 # common and efficient to use multiples of 2 for filters nb_epoch = 1 batch_size = 32 X, y = load(df, test) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=99) model = Sequential() model.add(Convolution2D(nb_filter = nb_filter, nb_row = 3, nb_col = 3, # border_mode = 'same', # this causes a crash, even though default is 'same'.... input_shape = X_train.shape[1:])) # must set input shape for first call to Convolutional2D model.add(Activation('relu')) # default convolutional activation function - should try Leaky ReLU and PReLU functions : http://arxiv.org/abs/1502.01852 model.add(MaxPooling2D()) # pooling 2x2 with stride 2 is default - reduces size of matrix by half in both dimensions # There is a new paper quesitoning the use of max pooling - finding that replacement with convolutional layers with increased stride works better model.add(Dropout(0.1)) model.add(Convolution2D(nb_filter = nb_filter * 2, nb_row = 2, nb_col = 2)) model.add(Activation('relu')) model.add(MaxPooling2D()) model.add(Dropout(0.2)) model.add(Convolution2D(nb_filter = nb_filter * 4, nb_row = 2, nb_col = 2)) model.add(Activation('relu')) model.add(MaxPooling2D()) model.add(Dropout(0.3)) model.add(Flatten()) # flatten the data before fully connected layer (reg. neural net) model.add(Dense(1024)) model.add(Activation('relu')) model.add(Dropout(0.4)) model.add(Dense(1024)) model.add(Activation('relu')) model.add(Dropout(0.5)) if len(flip_indices) == 12: model.add(Dense(30)) else: model.add(Dense(8)) model.compile(loss = 'mean_squared_error', # other objectives : http://keras.io/objectives/ optimizer = 'Adam') # discussion : http://cs231n.github.io/neural-networks-3/ : "Adam is currently recommended as the default algorithm to use" - cs231n dataGen = createDataGen(flip_indices = flip_indices, horizontal_flip = True) checkpointer = ModelCheckpoint(filepath="/tmp/weights.hdf5", verbose=1, save_best_only=True) earlystopping = EarlyStopping(monitor = 'val_loss', patience = 20, verbose = 0, mode = 'auto') model.fit_generator(dataGen.flow(X_train, y_train, batch_size = batch_size), nb_epoch = nb_epoch, samples_per_epoch = X_train.shape[0], validation_data = (X_test, y_test), callbacks = [checkpointer, earlystopping])
class TrainAllCnn(object): def __init__(self): # prepare trainigs data (X_train, y_train), (X_test, y_test) = cifar10.load_data() print('X_train shape:', X_train.shape) print(X_train.shape[0], 'train samples') print(X_test.shape[0], 'test samples') self.Y_train = np_utils.to_categorical(y_train, 10) self.Y_test = np_utils.to_categorical(y_test, 10) self.X_train = X_train.astype('float32') self.X_test = X_test.astype('float32') self.X_train /= 255 self.X_test /= 255 def build_model(self): # we want an sequential model self.model = Sequential() self.model.add(Convolution2D(96, 3, 3, border_mode='same', input_shape=(3, 32, 32))) self.model.add(Activation('relu')) self.model.add(Convolution2D(96, 3, 3, border_mode='same')) self.model.add(Activation('relu')) self.model.add(Convolution2D(96, 3, 3, subsample=(2,2), border_mode='same')) self.model.add(Activation('relu')) self.model.add(Convolution2D(192, 3, 3, border_mode='same')) self.model.add(Activation('relu')) self.model.add(Convolution2D(192, 3, 3, border_mode='same')) self.model.add(Activation('relu')) self.model.add(Convolution2D(96, 3, 3, subsample=(2, 2), border_mode='same')) self.model.add(Activation('relu')) self.model.add(MaxPooling2D(pool_size=(3, 3), strides=(2,2))) self.model.add(Convolution2D(192, 3, 3)) self.model.add(Activation('relu')) self.model.add(Convolution2D(192, 1, 1)) self.model.add(Activation('relu')) self.model.add(Convolution2D(10, 1, 1)) self.model.add(Activation('relu')) self.model.add(Flatten()) self.model.add(Activation('softmax')) # now we compile the model, asoptimizer we use stochastik gradient decent with momentum sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) self.model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) def train(self, batch, epoch): self.build_model() # now we fit the model # with 200 epochs this takes a while ... return self.model.fit(self.X_train, self.Y_train, batch_size=batch, nb_epoch=epoch, validation_data=(self.X_test, self.Y_test), shuffle=True) def train_argument_images(self, batch, epoch): self.build_model() datagen = ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=0, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True, vertical_flip=False, zoom_range=0.1) datagen.fit(self.X_train) return self.model.fit_generator(datagen.flow(self.X_train, self.Y_train, batch_size=batch), samples_per_epoch=self.X_train.shape[0], nb_epoch=epoch, validation_data=(self.X_test, self.Y_test)).model
def main(): ext = extension_from_parameters() out_dim = 1 loss = 'mse' metrics = None #metrics = ['accuracy'] if CATEGORICAL else None reshape = LOCALLY_CONNECTED_LAYERS is not None datagen = RegressionDataGenerator() train_gen = datagen.flow(batch_size=BATCH_SIZE, reshape=reshape) val_gen = datagen.flow(val=True, batch_size=BATCH_SIZE, reshape=reshape) val_gen2 = datagen.flow(val=True, batch_size=BATCH_SIZE, reshape=reshape) model = Sequential() if LOCALLY_CONNECTED_LAYERS: for layer in LOCALLY_CONNECTED_LAYERS: if layer: model.add(LocallyConnected1D(*layer, input_shape=(datagen.input_dim, 1), activation=ACTIVATION)) if POOL: model.add(MaxPooling1D(pool_length=POOL)) model.add(Flatten()) for layer in DENSE_LAYERS: if layer: model.add(Dense(layer, input_dim=datagen.input_dim, activation=ACTIVATION)) if DROP: model.add(Dropout(DROP)) model.add(Dense(out_dim)) model.summary() model.compile(loss=loss, optimizer='sgd', metrics=metrics) train_samples = int(datagen.n_train/BATCH_SIZE) * BATCH_SIZE val_samples = int(datagen.n_val/BATCH_SIZE) * BATCH_SIZE history = BestLossHistory(val_gen2, val_samples, ext) checkpointer = ModelCheckpoint(filepath='model'+ext+'.h5', save_best_only=True) model.fit_generator(train_gen, train_samples, nb_epoch = NB_EPOCH, validation_data = val_gen, nb_val_samples = val_samples, callbacks=[history, checkpointer])
def test_CallbackValData(): np.random.seed(1337) (X_train, y_train), (X_test, y_test) = get_test_data(num_train=train_samples, num_test=test_samples, input_shape=(input_dim,), classification=True, num_classes=num_class) y_test = np_utils.to_categorical(y_test) y_train = np_utils.to_categorical(y_train) model = Sequential() model.add(Dense(num_hidden, input_dim=input_dim, activation='relu')) model.add(Dense(num_class, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) cbk = callbacks.LambdaCallback(on_train_end=lambda x: 1) model.fit(X_train, y_train, batch_size=batch_size, validation_data=(X_test, y_test), callbacks=[cbk], epochs=1) def data_generator(train): if train: max_batch_index = len(X_train) // batch_size else: max_batch_index = len(X_test) // batch_size i = 0 while 1: if train: yield (X_train[i * batch_size: (i + 1) * batch_size], y_train[i * batch_size: (i + 1) * batch_size]) else: yield (X_test[i * batch_size: (i + 1) * batch_size], y_test[i * batch_size: (i + 1) * batch_size]) i += 1 i = i % max_batch_index cbk2 = callbacks.LambdaCallback(on_train_end=lambda x: 1) model.fit_generator(data_generator(True), len(X_train), epochs=1, validation_data=(X_test, y_test), callbacks=[cbk2]) # callback validation data should always have x, y, and sample weights assert len(cbk.validation_data) == len(cbk2.validation_data) == 3 assert cbk.validation_data[0] is cbk2.validation_data[0] assert cbk.validation_data[1] is cbk2.validation_data[1] assert cbk.validation_data[2].shape == cbk2.validation_data[2].shape
def test_multiprocessing_training(): reached_end = False arr_data = np.random.randint(0, 256, (500, 2)) arr_labels = np.random.randint(0, 2, 500) def myGenerator(): batch_size = 32 n_samples = 500 while True: batch_index = np.random.randint(0, n_samples - batch_size) start = batch_index end = start + batch_size X = arr_data[start: end] y = arr_labels[start: end] yield X, y # Build a NN model = Sequential() model.add(Dense(1, input_shape=(2, ))) model.compile(loss='mse', optimizer='adadelta') model.fit_generator(myGenerator(), samples_per_epoch=320, nb_epoch=1, verbose=1, max_q_size=10, nb_worker=4, pickle_safe=True) model.fit_generator(myGenerator(), samples_per_epoch=320, nb_epoch=1, verbose=1, max_q_size=10, pickle_safe=False) reached_end = True assert reached_end
def train(train_generator,train_size,input_num,dims_num): print("Start Train Job! ") start=time.time() inputs=InputLayer(input_shape=(input_num,dims_num),batch_size=batch_size) layer1=LSTM(128) output=Dense(2,activation="softmax",name="Output") optimizer=Adam() model=Sequential() model.add(inputs) model.add(layer1) model.add(Dropout(0.5)) model.add(output) call=TensorBoard(log_dir=log_dir,write_grads=True,histogram_freq=1) model.compile(optimizer,loss="categorical_crossentropy",metrics=["accuracy"]) model.fit_generator(train_generator,steps_per_epoch=train_size//batch_size,epochs=epochs_num,callbacks=[call]) # model.fit_generator(train_generator, steps_per_epoch=5, epochs=5, callbacks=[call]) model.save(model_dir) end=time.time() print("Over train job in %f s"%(end-start))
class SequenceTaggingMachine(object): def __init__(self, mask ): self.model = None self.mask = mask def train(self, train_corpus, valid_corpus, learning_param): self.model = Sequential() self.model.add(Embedding(train_corpus.source_cell_num(), 256, mask_zero = True)) self.model.add(Bidirectional(LSTM(128, return_sequences=True))) self.model.add(Bidirectional(LSTM(128, return_sequences=True))) self.model.add(TimeDistributed(Dense(input_dim=128, output_dim=train_corpus.target_cell_num()))) self.model.add(Activation('softmax')) self.model.compile(loss=lambda output,target: masked_categorical_crossentropy(output, target, self.mask), optimizer='rmsprop', metrics=[lambda y_true, y_pred: masked_categorical_accuracy(y_true, y_pred, self.mask)]) logging.debug("Preparing data iter") problem = SequenceTaggingProblem(train_corpus) data_train = BucketIter(problem, learning_param.batch_size, max_pad_num=learning_param.max_pad) val_problem = SequenceTaggingProblem(valid_corpus) data_val = BucketIter(val_problem, learning_param.batch_size, max_pad_num=learning_param.max_pad) checkpointer = ModelCheckpoint(filepath="weights.{epoch:03d}-{val_loss:.2f}.hdf5", verbose=1) logging.debug("Begin train model") self.model.fit_generator(bucket_iter_adapter(data_train,train_corpus.target_cell_num()), samples_per_epoch=train_corpus.corpus_size(), nb_epoch=100, verbose=1, validation_data=bucket_iter_adapter(data_val, train_corpus.target_cell_num()), nb_val_samples = valid_corpus.corpus_size(), callbacks=[checkpointer]) print "Model is trained"
def test_multiprocessing_training_fromfile(in_tmpdir): arr_data = np.random.randint(0, 256, (50, 2)) arr_labels = np.random.randint(0, 2, 50) np.savez('data.npz', **{'data': arr_data, 'labels': arr_labels}) def custom_generator(): batch_size = 10 n_samples = 50 arr = np.load('data.npz') while True: batch_index = np.random.randint(0, n_samples - batch_size) start = batch_index end = start + batch_size X = arr['data'][start: end] y = arr['labels'][start: end] yield X, y # Build a NN model = Sequential() model.add(Dense(1, input_shape=(2, ))) model.compile(loss='mse', optimizer='adadelta') model.fit_generator(custom_generator(), steps_per_epoch=5, epochs=1, verbose=1, max_queue_size=10, workers=2, use_multiprocessing=True) model.fit_generator(custom_generator(), steps_per_epoch=5, epochs=1, verbose=1, max_queue_size=10, use_multiprocessing=False) os.remove('data.npz')
def rnn_gru(float_data, lookback=1440, step=6): model = Sequential() model.add(layers.GRU(32, input_shape=(None, float_data.shape[-1]))) model.add(layers.Dense(1)) model.compile(optimizer=RMSprop(), loss='mae') history = model.fit_generator(train_gen, steps_per_epoch=500, epochs=20, validation_data=val_gen, validation_steps=val_steps) draw_histroy(history)
def test_multithreading_from_file(): arr_data = np.random.randint(0, 256, (50, 2)) arr_labels = np.random.randint(0, 2, 50) np.savez('data_threads.npz', **{'data': arr_data, 'labels': arr_labels}) @threadsafe_generator def custom_generator(): batch_size = 10 n_samples = 50 arr = np.load('data_threads.npz') while True: batch_index = np.random.randint(0, n_samples - batch_size) start = batch_index end = start + batch_size X = arr['data'][start: end] y = arr['labels'][start: end] yield X, y # Build a NN model = Sequential() model.add(Dense(1, input_shape=(2,))) model.compile(loss='mse', optimizer='adadelta') # - Produce data on 4 worker threads, consume on main thread: # - All worker threads share the SAME generator model.fit_generator(custom_generator(), steps_per_epoch=STEPS_PER_EPOCH, epochs=1, verbose=1, validation_steps=None, max_queue_size=10, workers=WORKERS, use_multiprocessing=False) # - Produce data on 1 worker thread, consume on main thread: # - Worker thread is the only thread running the generator model.fit_generator(custom_generator(), steps_per_epoch=STEPS_PER_EPOCH, epochs=1, verbose=1, validation_steps=None, max_queue_size=10, workers=1, use_multiprocessing=False) # - Produce and consume data without a queue on main thread # - Make sure the value of `use_multiprocessing` is ignored model.fit_generator(custom_generator(), steps_per_epoch=STEPS_PER_EPOCH, epochs=1, verbose=1, validation_steps=None, max_queue_size=10, workers=0, use_multiprocessing=False) os.remove('data_threads.npz')
def train_xy(epochs=50, batch_size=32, h=256, w=256, ch=3, train_p=0.8, valid_p=0.1): print("Compiling Model") t_comp = time() model = Sequential() # reshape input to ch, h, w (no sample axis) model.add(Reshape(dims=(h, w, ch), input_shape=(ch * h * w,))) model.add(Permute((3, 1, 2))) # add conv layers model.add(Convolution2D(16, 3, 3, init="glorot_uniform", activation="relu", subsample=(1, 1))) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Convolution2D(32, 3, 3, init="glorot_uniform", activation="relu", subsample=(1, 1))) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Convolution2D(64, 3, 3, init="glorot_uniform", activation="relu", subsample=(1, 1))) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Convolution2D(64, 3, 3, init="glorot_uniform", activation="relu", subsample=(1, 1))) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(output_dim=2000, init="glorot_uniform", activation="relu", W_regularizer=l2(0.001))) model.add(Dropout(0.5)) model.add(Dense(output_dim=2000, init="glorot_uniform", activation="relu", W_regularizer=l2(0.001))) model.add(Dropout(0.5)) model.add(Dense(output_dim=4, init="glorot_uniform", activation="relu")) model.compile(optimizer="rmsprop", loss="mse") t_train = time() print("Took %.1fs" % (t_train - t_comp)) # split dataset i_test = int(train_p * nrow) / batch_size * batch_size i_valid = int(i_test * (1 - valid_p)) / batch_size * batch_size X_train, Y_train = X_[:i_valid,], Y_[:i_valid,] X_valid, Y_valid = X_[i_valid:i_test,], Y_[i_valid:i_test,] # naive fitting to lower rmse faster hist = model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=10, verbose=1, validation_split=0.1) print(hist) # fit by batch using generator! img_aug = image_augmentor(X_, Y_, i_valid) hist = model.fit_generator( generator=img_aug, samples_per_epoch=i_valid, nb_epoch=5000, verbose=1, validation_data=(X_valid, Y_valid), nb_worker=1, ) rmse_test = model.evaluate(X_[i_test:,], Y_[i_test:,]) print("Test RMSE: %.4f" % rmse_test) # save model model_json = model.to_json() open(path_img + "locate/model_116.json", "w").write(model_json) model.save_weights(path_img + "locate/model_116_weights.h5")
def test_stop_training_csv(tmpdir): np.random.seed(1337) fp = str(tmpdir / 'test.csv') (X_train, y_train), (X_test, y_test) = get_test_data(num_train=train_samples, num_test=test_samples, input_shape=(input_dim,), classification=True, num_classes=num_classes) y_test = np_utils.to_categorical(y_test) y_train = np_utils.to_categorical(y_train) cbks = [callbacks.TerminateOnNaN(), callbacks.CSVLogger(fp)] model = Sequential() for _ in range(5): model.add(Dense(num_hidden, input_dim=input_dim, activation='relu')) model.add(Dense(num_classes, activation='linear')) model.compile(loss='mean_squared_error', optimizer='rmsprop') def data_generator(): i = 0 max_batch_index = len(X_train) // batch_size tot = 0 while 1: if tot > 3 * len(X_train): yield (np.ones([batch_size, input_dim]) * np.nan, np.ones([batch_size, num_classes]) * np.nan) else: yield (X_train[i * batch_size: (i + 1) * batch_size], y_train[i * batch_size: (i + 1) * batch_size]) i += 1 tot += 1 i = i % max_batch_index history = model.fit_generator(data_generator(), len(X_train) // batch_size, validation_data=(X_test, y_test), callbacks=cbks, epochs=20) loss = history.history['loss'] assert len(loss) > 1 assert loss[-1] == np.inf or np.isnan(loss[-1]) values = [] with open(fp) as f: for x in reader(f): values.append(x) assert 'nan' in values[-1], 'The last epoch was not logged.' os.remove(fp)