Python keras.layers.core.Activation() Examples
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Example #1
Source File: test_tasks.py From CAPTCHA-breaking with MIT License | 7 votes |
def test_temporal_clf(self): print('temporal classification data:') (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(5,10), classification=True, nb_class=2) print('X_train:', X_train.shape) print('X_test:', X_test.shape) print('y_train:', y_train.shape) print('y_test:', y_test.shape) y_train = to_categorical(y_train) y_test = to_categorical(y_test) model = Sequential() model.add(GRU(X_train.shape[-1], y_train.shape[-1])) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='adadelta') history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), show_accuracy=True, verbose=2) self.assertTrue(history.history['val_acc'][-1] > 0.9)
Example #2
Source File: pspnet.py From keras-image-segmentation with MIT License | 7 votes |
def conv_block(input_tensor, filters, strides, d_rates): x = Conv2D(filters[0], kernel_size=1, dilation_rate=d_rates[0])(input_tensor) x = BatchNormalization()(x) x = Activation('relu')(x) x = Conv2D(filters[1], kernel_size=3, strides=strides, padding='same', dilation_rate=d_rates[1])(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = Conv2D(filters[2], kernel_size=1, dilation_rate=d_rates[2])(x) x = BatchNormalization()(x) shortcut = Conv2D(filters[2], kernel_size=1, strides=strides)(input_tensor) shortcut = BatchNormalization()(shortcut) x = add([x, shortcut]) x = Activation('relu')(x) return x
Example #3
Source File: pspnet.py From keras-image-segmentation with MIT License | 7 votes |
def identity_block(input_tensor, filters, d_rates): x = Conv2D(filters[0], kernel_size=1, dilation_rate=d_rates[0])(input_tensor) x = BatchNormalization()(x) x = Activation('relu')(x) x = Conv2D(filters[1], kernel_size=3, padding='same', dilation_rate=d_rates[1])(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = Conv2D(filters[2], kernel_size=1, dilation_rate=d_rates[2])(x) x = BatchNormalization()(x) x = add([x, input_tensor]) x = Activation('relu')(x) return x
Example #4
Source File: pspnet.py From keras-image-segmentation with MIT License | 7 votes |
def conv_block(input_tensor, filters, strides, d_rates): x = Conv2D(filters[0], kernel_size=1, dilation_rate=d_rates[0])(input_tensor) x = BatchNormalization()(x) x = Activation('relu')(x) x = Conv2D(filters[1], kernel_size=3, strides=strides, padding='same', dilation_rate=d_rates[1])(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = Conv2D(filters[2], kernel_size=1, dilation_rate=d_rates[2])(x) x = BatchNormalization()(x) shortcut = Conv2D(filters[2], kernel_size=1, strides=strides)(input_tensor) shortcut = BatchNormalization()(shortcut) x = add([x, shortcut]) x = Activation('relu')(x) return x
Example #5
Source File: example.py From residual_block_keras with GNU General Public License v3.0 | 7 votes |
def get_residual_model(is_mnist=True, img_channels=1, img_rows=28, img_cols=28): model = keras.models.Sequential() first_layer_channel = 128 if is_mnist: # size to be changed to 32,32 model.add(ZeroPadding2D((2,2), input_shape=(img_channels, img_rows, img_cols))) # resize (28,28)-->(32,32) # the first conv model.add(Convolution2D(first_layer_channel, 3, 3, border_mode='same')) else: model.add(Convolution2D(first_layer_channel, 3, 3, border_mode='same', input_shape=(img_channels, img_rows, img_cols))) model.add(Activation('relu')) # [residual-based Conv layers] residual_blocks = design_for_residual_blocks(num_channel_input=first_layer_channel) model.add(residual_blocks) model.add(BatchNormalization(axis=1)) model.add(Activation('relu')) # [Classifier] model.add(Flatten()) model.add(Dense(nb_classes)) model.add(Activation('softmax')) # [END] return model
Example #6
Source File: psp_temp.py From keras-image-segmentation with MIT License | 7 votes |
def identity_block(input_tensor, filters, d_rates): x = Conv2D(filters[0], kernel_size=1, dilation_rate=d_rates[0])(input_tensor) x = BatchNormalization()(x) x = Activation('relu')(x) x = Conv2D(filters[1], kernel_size=3, padding='same', dilation_rate=d_rates[1])(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = Conv2D(filters[2], kernel_size=1, dilation_rate=d_rates[2])(x) x = BatchNormalization()(x) x = add([x, input_tensor]) x = Activation('relu')(x) return x
Example #7
Source File: psp_temp.py From keras-image-segmentation with MIT License | 7 votes |
def conv_block(input_tensor, filters, strides, d_rates): x = Conv2D(filters[0], kernel_size=1, dilation_rate=d_rates[0])(input_tensor) x = BatchNormalization()(x) x = Activation('relu')(x) x = Conv2D(filters[1], kernel_size=3, strides=strides, padding='same', dilation_rate=d_rates[1])(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = Conv2D(filters[2], kernel_size=1, dilation_rate=d_rates[2])(x) x = BatchNormalization()(x) shortcut = Conv2D(filters[2], kernel_size=1, strides=strides)(input_tensor) shortcut = BatchNormalization()(shortcut) x = add([x, shortcut]) x = Activation('relu')(x) return x
Example #8
Source File: co_lstm_predict_sequence.py From copper_price_forecast with GNU General Public License v3.0 | 6 votes |
def build_model(): """ 定义模型 """ model = Sequential() model.add(LSTM(units=Conf.LAYERS[1], input_shape=(Conf.LAYERS[1], Conf.LAYERS[0]), return_sequences=True)) model.add(Dropout(0.2)) model.add(LSTM(Conf.LAYERS[2], return_sequences=False)) model.add(Dropout(0.2)) model.add(Dense(units=Conf.LAYERS[3])) # model.add(BatchNormalization(weights=None, epsilon=1e-06, momentum=0.9)) model.add(Activation("tanh")) # act = PReLU(alpha_initializer='zeros', weights=None) # act = LeakyReLU(alpha=0.3) # model.add(act) start = time.time() model.compile(loss="mse", optimizer="rmsprop") print("> Compilation Time : ", time.time() - start) return model
Example #9
Source File: gc_densenet.py From keras-global-context-networks with MIT License | 6 votes |
def __transition_block(ip, nb_filter, compression=1.0, weight_decay=1e-4): ''' Apply BatchNorm, Relu 1x1, Conv2D, optional compression, dropout and Maxpooling2D Args: ip: keras tensor nb_filter: number of filters compression: calculated as 1 - reduction. Reduces the number of feature maps in the transition block. dropout_rate: dropout rate weight_decay: weight decay factor Returns: keras tensor, after applying batch_norm, relu-conv, dropout, maxpool ''' concat_axis = 1 if K.image_data_format() == 'channels_first' else -1 x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(ip) x = Activation('relu')(x) x = Conv2D(int(nb_filter * compression), (1, 1), kernel_initializer='he_normal', padding='same', use_bias=False, kernel_regularizer=l2(weight_decay))(x) x = AveragePooling2D((2, 2), strides=(2, 2))(x) # global context block x = global_context_block(x) return x
Example #10
Source File: pspnet.py From keras-image-segmentation with MIT License | 6 votes |
def identity_block(input_tensor, filters, d_rates): x = Conv2D(filters[0], kernel_size=1, dilation_rate=d_rates[0])(input_tensor) x = BatchNormalization()(x) x = Activation('relu')(x) x = Conv2D(filters[1], kernel_size=3, padding='same', dilation_rate=d_rates[1])(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = Conv2D(filters[2], kernel_size=1, dilation_rate=d_rates[2])(x) x = BatchNormalization()(x) x = add([x, input_tensor]) x = Activation('relu')(x) return x
Example #11
Source File: cnn.py From DeepFashion with Apache License 2.0 | 6 votes |
def model_create(input_shape, num_classes): logging.debug('input_shape {}'.format(input_shape)) model = Sequential() model.add(Conv2D(32, (3, 3), border_mode='same', input_shape=input_shape)) model.add(Activation('relu')) model.add(Conv2D(32, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(128)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(num_classes)) model.add(Activation('softmax')) # use binary_crossentropy if has just 2 prediction yes or no model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']) return model
Example #12
Source File: model_zoo.py From visual_turing_test-tutorial with MIT License | 6 votes |
def create(self): self._input_name = 'text' self._output_name = 'output' self.add_input( name=self._input_name, input_shape=(self._config.max_input_time_steps, self._config.input_dim,)) self.inputs['text'].input = T.imatrix() self.add_node(Embedding( self._config.input_dim, self._config.textual_embedding_dim, mask_zero=True), name='embedding', input='text') self.add_node( self._config.recurrent_encoder( self._config.hidden_state_dim, return_sequences=False, go_backwards=self._config.go_backwards), name='recurrent', input='embedding') self.add_node(Dropout(0.5), name='dropout', input='recurrent') self.add_node(Dense(self._config.output_dim), name='dense', input='dropout') self.add_node(Activation('softmax'), name='softmax', input='dense') self.add_output(name=self._output_name, input='softmax')
Example #13
Source File: test_graph_model.py From CAPTCHA-breaking with MIT License | 6 votes |
def test_1o_1i_2(self): print('test a more complex non-sequential graph with 1 input and 1 output') graph = Graph() graph.add_input(name='input1', ndim=2) graph.add_node(Dense(32, 16), name='dense1', input='input1') graph.add_node(Dense(32, 4), name='dense2-0', input='input1') graph.add_node(Activation('relu'), name='dense2', input='dense2-0') graph.add_node(Dense(4, 16), name='dense3', input='dense2') graph.add_node(Dense(16, 4), name='dense4', inputs=['dense1', 'dense3'], merge_mode='sum') graph.add_output(name='output1', inputs=['dense2', 'dense4'], merge_mode='sum') graph.compile('rmsprop', {'output1': 'mse'}) history = graph.fit({'input1': X_train, 'output1': y_train}, nb_epoch=10) out = graph.predict({'input1': X_train}) assert(type(out == dict)) assert(len(out) == 1) loss = graph.test_on_batch({'input1': X_test, 'output1': y_test}) loss = graph.train_on_batch({'input1': X_test, 'output1': y_test}) loss = graph.evaluate({'input1': X_test, 'output1': y_test}) print(loss) assert(loss < 2.5) graph.get_config(verbose=1)
Example #14
Source File: lstm.py From copper_price_forecast with GNU General Public License v3.0 | 6 votes |
def build_model(layers): """ 模型定义 """ model = Sequential() model.add(LSTM(units=layers[1], input_shape=(layers[1], layers[0]), return_sequences=True)) model.add(Dropout(0.2)) model.add(LSTM(layers[2], return_sequences=False)) model.add(Dropout(0.2)) model.add(Dense(units=layers[3])) model.add(Activation("tanh")) start = time.time() model.compile(loss="mse", optimizer="rmsprop") print("> Compilation Time : ", time.time() - start) return model
Example #15
Source File: co_lstm_predict_day.py From copper_price_forecast with GNU General Public License v3.0 | 6 votes |
def build_model(): """ 定义模型 """ model = Sequential() model.add(LSTM(units=Conf.LAYERS[1], input_shape=(Conf.LAYERS[1], Conf.LAYERS[0]), return_sequences=True)) model.add(Dropout(0.2)) model.add(LSTM(Conf.LAYERS[2], return_sequences=False)) model.add(Dropout(0.2)) model.add(Dense(units=Conf.LAYERS[3])) # model.add(BatchNormalization(weights=None, epsilon=1e-06, momentum=0.9)) model.add(Activation("tanh")) # act = PReLU(alpha_initializer='zeros', weights=None) # act = LeakyReLU(alpha=0.3) # model.add(act) start = time.time() model.compile(loss="mse", optimizer="rmsprop") print("> Compilation Time : ", time.time() - start) return model
Example #16
Source File: model_zoo.py From visual_turing_test-tutorial with MIT License | 6 votes |
def create(self): self.textual_embedding(self, mask_zero=True) self.stacked_RNN(self) self.add(self._config.recurrent_encoder( self._config.hidden_state_dim, return_sequences=False, go_backwards=self._config.go_backwards)) self.add(Dropout(0.5)) self.add(RepeatVector(self._config.max_output_time_steps)) self.add(self._config.recurrent_decoder( self._config.hidden_state_dim, return_sequences=True)) self.add(Dropout(0.5)) self.add(TimeDistributedDense(self._config.output_dim)) self.add(Activation('softmax')) ### # Multimodal models ###
Example #17
Source File: model_zoo.py From visual_turing_test-tutorial with MIT License | 6 votes |
def create(self): assert self._config.merge_mode in ['max', 'ave', 'sum'], \ 'Merge mode of this model is either max, ave or sum' model_list = [None] * self._config.language_cnn_views for j in xrange(1,self._config.language_cnn_views+1): current_view = Sequential() self.textual_embedding(current_view, mask_zero=True) current_view.add(Convolution1D( nb_filter=self._config.language_cnn_filters, filter_length=j, border_mode='valid', activation=self._config.language_cnn_activation, subsample_length=1)) self.temporal_pooling(current_view) model_list[j-1] = current_view self.add(Merge(model_list, mode='concat')) self.deep_mlp() self.add(Dense(self._config.output_dim)) self.add(Activation('softmax'))
Example #18
Source File: RNN-example_using_keras.py From QUANTAXIS with MIT License | 6 votes |
def build_model(layers): model = Sequential() model.add(LSTM( input_dim=layers[0], output_dim=layers[1], return_sequences=True)) model.add(Dropout(0.2)) model.add(LSTM( layers[2], return_sequences=False)) model.add(Dropout(0.2)) model.add(Dense( output_dim=layers[2])) model.add(Activation("linear")) start = time.time() model.compile(loss="mse", optimizer="rmsprop", metrics=['accuracy']) print("Compilation Time : ", time.time() - start) return model
Example #19
Source File: model_zoo.py From visual_turing_test-tutorial with MIT License | 6 votes |
def create(self): assert self._config.merge_mode in ['max', 'ave', 'sum'], \ 'Merge mode of this model is either max, ave or sum' self.textual_embedding(self, mask_zero=False) #self.textual_embedding(self, mask_zero=True) self.add(MaskedConvolution1D( nb_filter=self._config.language_cnn_filters, filter_length=self._config.language_cnn_filter_length, border_mode='valid', activation=self._config.language_cnn_activation, subsample_length=1)) self.temporal_pooling(self) #self.add(DropMask()) self.deep_mlp() self.add(Dense(self._config.output_dim)) self.add(Activation('softmax'))
Example #20
Source File: model_zoo.py From visual_turing_test-tutorial with MIT License | 6 votes |
def create(self): self.textual_embedding(self, mask_zero=False) self.add(Convolution1D( nb_filter=self._config.language_cnn_filters, filter_length=self._config.language_cnn_filter_length, border_mode='valid', activation=self._config.language_cnn_activation, subsample_length=1)) #self.add(MaxPooling1D(pool_length=self._config.language_max_pool_length)) self.add(self._config.recurrent_encoder( self._config.hidden_state_dim, return_sequences=False, go_backwards=False)) self.deep_mlp() self.add(Dense(self._config.output_dim)) self.add(Activation('softmax'))
Example #21
Source File: test_tasks.py From CAPTCHA-breaking with MIT License | 6 votes |
def test_vector_clf(self): nb_hidden = 10 print('vector classification data:') (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(10,), classification=True, nb_class=2) print('X_train:', X_train.shape) print('X_test:', X_test.shape) print('y_train:', y_train.shape) print('y_test:', y_test.shape) y_train = to_categorical(y_train) y_test = to_categorical(y_test) model = Sequential() model.add(Dense(X_train.shape[-1], nb_hidden)) model.add(Activation('relu')) model.add(Dense(nb_hidden, y_train.shape[-1])) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='rmsprop') history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), show_accuracy=True, verbose=2) print(history.history) self.assertTrue(history.history['val_acc'][-1] > 0.9)
Example #22
Source File: model_zoo.py From visual_turing_test-tutorial with MIT License | 6 votes |
def create(self): assert self._config.textual_embedding_dim == 0, \ 'Embedding cannot be learnt but must be fixed' language_forward = Sequential() language_forward.add(self._config.recurrent_encoder( self._config.hidden_state_dim, return_sequences=False, input_shape=(self._config.max_input_time_steps, self._config.input_dim))) self.language_forward = language_forward language_backward = Sequential() language_backward.add(self._config.recurrent_encoder( self._config.hidden_state_dim, return_sequences=False, go_backwards=True, input_shape=(self._config.max_input_time_steps, self._config.input_dim))) self.language_backward = language_backward self.add(Merge([language_forward, language_backward])) self.deep_mlp() self.add(Dense(self._config.output_dim)) self.add(Activation('softmax'))
Example #23
Source File: captcha_gan.py From Intelligent-Projects-Using-Python with MIT License | 6 votes |
def discriminator(img_dim,alpha=0.2): model = Sequential() model.add( Conv2D(64, kernel_size=5,strides=2, padding='same', input_shape=img_dim) ) model.add(LeakyReLU(alpha)) model.add(Conv2D(128,kernel_size=5,strides=2,padding='same')) model.add(BatchNormalization()) model.add(LeakyReLU(alpha)) model.add(Conv2D(256,kernel_size=5,strides=2,padding='same')) model.add(BatchNormalization()) model.add(LeakyReLU(alpha)) model.add(Flatten()) model.add(Dense(1)) model.add(Activation('sigmoid')) return model # Define a combination of Generator and Discriminator
Example #24
Source File: test_tasks.py From CAPTCHA-breaking with MIT License | 6 votes |
def test_vector_reg(self): nb_hidden = 10 print('vector regression data:') (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(10,), output_shape=(2,), classification=False) print('X_train:', X_train.shape) print('X_test:', X_test.shape) print('y_train:', y_train.shape) print('y_test:', y_test.shape) model = Sequential() model.add(Dense(X_train.shape[-1], nb_hidden)) model.add(Activation('tanh')) model.add(Dense(nb_hidden, y_train.shape[-1])) model.compile(loss='hinge', optimizer='adagrad') history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), verbose=2) self.assertTrue(history.history['val_loss'][-1] < 0.9)
Example #25
Source File: model_zoo.py From visual_turing_test-tutorial with MIT License | 6 votes |
def deep_mlp(self): """ Deep Multilayer Perceptrop. """ if self._config.num_mlp_layers == 0: self.add(Dropout(0.5)) else: for j in xrange(self._config.num_mlp_layers): self.add(Dense(self._config.mlp_hidden_dim)) if self._config.mlp_activation == 'elu': self.add(ELU()) elif self._config.mlp_activation == 'leaky_relu': self.add(LeakyReLU()) elif self._config.mlp_activation == 'prelu': self.add(PReLU()) else: self.add(Activation(self._config.mlp_activation)) self.add(Dropout(0.5))
Example #26
Source File: test_tasks.py From CAPTCHA-breaking with MIT License | 6 votes |
def test_img_clf(self): print('image classification data:') (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(3, 32, 32), classification=True, nb_class=2) print('X_train:', X_train.shape) print('X_test:', X_test.shape) print('y_train:', y_train.shape) print('y_test:', y_test.shape) y_train = to_categorical(y_train) y_test = to_categorical(y_test) model = Sequential() model.add(Convolution2D(32, 3, 32, 32)) model.add(Activation('sigmoid')) model.add(Flatten()) model.add(Dense(32, y_test.shape[-1])) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='sgd') history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), show_accuracy=True, verbose=2) self.assertTrue(history.history['val_acc'][-1] > 0.9)
Example #27
Source File: hyperparam_optimization.py From elephas with MIT License | 5 votes |
def model(x_train, y_train, x_test, y_test): """Model providing function: Create Keras model with double curly brackets dropped-in as needed. Return value has to be a valid python dictionary with two customary keys: - loss: Specify a numeric evaluation metric to be minimized - status: Just use STATUS_OK and see hyperopt documentation if not feasible The last one is optional, though recommended, namely: - model: specify the model just created so that we can later use it again. """ from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation from keras.optimizers import RMSprop keras_model = Sequential() keras_model.add(Dense(512, input_shape=(784,))) keras_model.add(Activation('relu')) keras_model.add(Dropout({{uniform(0, 1)}})) keras_model.add(Dense({{choice([256, 512, 1024])}})) keras_model.add(Activation('relu')) keras_model.add(Dropout({{uniform(0, 1)}})) keras_model.add(Dense(10)) keras_model.add(Activation('softmax')) rms = RMSprop() keras_model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['acc']) keras_model.fit(x_train, y_train, batch_size={{choice([64, 128])}}, epochs=1, verbose=2, validation_data=(x_test, y_test)) score, acc = keras_model.evaluate(x_test, y_test, verbose=0) print('Test accuracy:', acc) return {'loss': -acc, 'status': STATUS_OK, 'model': keras_model.to_yaml(), 'weights': pickle.dumps(keras_model.get_weights())} # Create Spark context
Example #28
Source File: generator.py From Generative-Adversarial-Networks-Cookbook with MIT License | 5 votes |
def model(self): # Input input_layer = Input(shape=self.SHAPE) x = Convolution2D(64, 3,3, border_mode='same',activation='relu')(input_layer) # ResNet Block 1 res_x_input_1 = Conv2D(64, (3,3), border_mode='same',activation='relu')(x) x = Convolution2D(64, 3,3, border_mode='same',activation='relu')(res_x_input_1) x = layers.Add()([res_x_input_1,x]) x = Activation('relu')(x) # ResNet Block 2 res_x_input_2 = Conv2D(64, (3,3), border_mode='same',activation='relu')(x) x = Convolution2D(64, 3,3, border_mode='same',activation='relu')(res_x_input_2) x = layers.Add()([res_x_input_2,x]) x = Activation('relu')(x) # ResNet Block 3 res_x_input_3 = Conv2D(64, (3,3), border_mode='same',activation='relu')(x) x = Convolution2D(64, 3,3, border_mode='same',activation='relu')(res_x_input_3) x = layers.Add()([res_x_input_3,x]) x = Activation('relu')(x) # ResNet Block 4 res_x_input_4 = Conv2D(64, (3,3), border_mode='same',activation='relu')(x) x = Convolution2D(64, 3,3, border_mode='same',activation='relu')(res_x_input_4) x = layers.Add()([res_x_input_4,x]) x = Activation('relu')(x) output_layer = Convolution2D(self.C,1,1, border_mode='same',activation='tanh')(x) return Model(input_layer,output_layer)
Example #29
Source File: captcha_gan.py From Intelligent-Projects-Using-Python with MIT License | 5 votes |
def generator(input_dim,alpha=0.2): model = Sequential() model.add(Dense(input_dim=input_dim, output_dim=4*4*512)) model.add(Reshape(target_shape=(4,4,512))) model.add(BatchNormalization()) model.add(LeakyReLU(alpha)) model.add(Conv2DTranspose(256, kernel_size=5, strides=2, padding='same')) model.add(BatchNormalization()) model.add(LeakyReLU(alpha)) model.add(Conv2DTranspose(128, kernel_size=5, strides=2, padding='same')) model.add(BatchNormalization()) model.add(LeakyReLU(alpha)) model.add(Conv2DTranspose(3, kernel_size=5, strides=2, padding='same')) model.add(Activation('tanh')) return model #Define the Discriminator Network
Example #30
Source File: classifier.py From stock-price-prediction with MIT License | 5 votes |
def performRNNlass(X_train, y_train): X_train = numpy.reshape(numpy.array(X_train), (X_train.shape[0], 1, X_train.shape[1])) model = Sequential() model.add(LSTM( 128, input_shape=(None, X_train.shape[2]), return_sequences=True)) model.add(Dropout(0.2)) model.add(LSTM( 100, return_sequences=False)) model.add(Dropout(0.2)) model.add(Dense( units=1)) model.add(Activation('sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit( X_train, y_train, batch_size=64, epochs=64, validation_split=0.1) return model