Python keras.models.Sequential() Examples
The following are code examples for showing how to use keras.models.Sequential(). They are from open source Python projects. You can vote up the examples you like or vote down the ones you don't like.
Example 1
| Project: speed_estimation Author: NeilNie File: simple_conv.py MIT License | 13 votes |
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def commaai_model(self):
model = Sequential()
model.add(Lambda(lambda x: x / 127.5 - 1., input_shape=(configs.IMG_HEIGHT, configs.IMG_WIDTH, 3), output_shape=(configs.IMG_HEIGHT, configs.IMG_WIDTH, 3)))
model.add(Conv2D(16, (8, 8), strides=4, padding="same"))
model.add(ELU())
model.add(Conv2D(32, (5, 5), strides=2, padding="same"))
model.add(ELU())
model.add(Conv2D(64, (5, 5), strides=2, padding="same"))
model.add(Flatten())
model.add(Dropout(.2))
model.add(ELU())
model.add(Dense(512))
model.add(Dropout(.5))
model.add(ELU())
model.add(Dense(1))
sgd = SGD(lr=0.00001, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='mean_squared_error')
# print('steering model is created and compiled...')
return model
Example 2
| Project: keras-anomaly-detection Author: chen0040 File: recurrent.py MIT License | 9 votes |
|
def create_model(time_window_size, metric):
model = Sequential()
model.add(Conv1D(filters=256, kernel_size=5, padding='same', activation='relu',
input_shape=(time_window_size, 1)))
model.add(MaxPooling1D(pool_size=4))
model.add(LSTM(64))
model.add(Dense(units=time_window_size, activation='linear'))
model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric])
# model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric])
# model.compile(optimizer="sgd", loss="mse", metrics=[metric])
print(model.summary())
return model
Example 3
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 7 votes |
|
def test_keras_import(self):
# Pad 1D
model = Sequential()
model.add(ZeroPadding1D(2, input_shape=(224, 3)))
model.add(Conv1D(32, 7, strides=2))
model.build()
self.pad_test(model, 'pad_w', 2)
# Pad 2D
model = Sequential()
model.add(ZeroPadding2D(2, input_shape=(224, 224, 3)))
model.add(Conv2D(32, 7, strides=2))
model.build()
self.pad_test(model, 'pad_w', 2)
# Pad 3D
model = Sequential()
model.add(ZeroPadding3D(2, input_shape=(224, 224, 224, 3)))
model.add(Conv3D(32, 7, strides=2))
model.build()
self.pad_test(model, 'pad_w', 2)
# ********** Export json tests **********
# ********** Data Layers Test **********
Example 4
| Project: Anamoly-Detection Author: msmsk05 File: gaal_base.py BSD 2-Clause "Simplified" License | 7 votes |
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def create_generator(latent_size): # pragma: no cover
"""Create the generator of the GAN for a given latent size.
Parameters
----------
latent_size : int
The size of the latent space of the generator
Returns
-------
D : Keras model() object
Returns a model() object.
"""
gen = Sequential()
gen.add(Dense(latent_size, input_dim=latent_size, activation='relu',
kernel_initializer=keras.initializers.Identity(
gain=1.0)))
gen.add(Dense(latent_size, activation='relu',
kernel_initializer=keras.initializers.Identity(
gain=1.0)))
latent = Input(shape=(latent_size,))
fake_data = gen(latent)
return Model(latent, fake_data)
Example 5
| Project: sanctuary Author: bzamecnik File: mnist.py MIT License | 6 votes |
|
def create_fc_model(input_shape, nb_classes, nb_layers, layer_width, dropout, activation):
model = Sequential()
model.add(Reshape((input_shape[0] * input_shape[1],), input_shape=input_shape))
for i in range(nb_layers - 1):
model.add(Dense(layer_width))
model.add(Activation(activation))
if dropout > 0:
model.add(Dropout(dropout))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
Example 6
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 6 votes |
|
def test_keras_import(self):
# Conv 1D
model = Sequential()
model.add(Conv1D(32, 10, kernel_regularizer=regularizers.l2(0.01),
bias_regularizer=regularizers.l2(0.01),
activity_regularizer=regularizers.l2(0.01), kernel_constraint='max_norm',
bias_constraint='max_norm', activation='relu', input_shape=(10, 1)))
model.build()
self.keras_param_test(model, 1, 9)
# Conv 2D
model = Sequential()
model.add(Conv2D(32, (3, 3), kernel_regularizer=regularizers.l2(0.01),
bias_regularizer=regularizers.l2(0.01),
activity_regularizer=regularizers.l2(0.01), kernel_constraint='max_norm',
bias_constraint='max_norm', activation='relu', input_shape=(16, 16, 1)))
model.build()
self.keras_param_test(model, 1, 13)
# Conv 3D
model = Sequential()
model.add(Conv3D(32, (3, 3, 3), kernel_regularizer=regularizers.l2(0.01),
bias_regularizer=regularizers.l2(0.01),
activity_regularizer=regularizers.l2(0.01), kernel_constraint='max_norm',
bias_constraint='max_norm', activation='relu', input_shape=(16, 16, 16, 1)))
model.build()
self.keras_param_test(model, 1, 17)
Example 7
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 6 votes |
|
def test_keras_import(self):
# Conv 1D
model = Sequential()
model.add(LocallyConnected1D(32, 3, kernel_regularizer=regularizers.l2(0.01),
bias_regularizer=regularizers.l2(0.01),
activity_regularizer=regularizers.l2(0.01), kernel_constraint='max_norm',
bias_constraint='max_norm', activation='relu', input_shape=(16, 10)))
model.build()
self.keras_param_test(model, 1, 12)
# Conv 2D
model = Sequential()
model.add(LocallyConnected2D(32, (3, 3), kernel_regularizer=regularizers.l2(0.01),
bias_regularizer=regularizers.l2(0.01),
activity_regularizer=regularizers.l2(0.01), kernel_constraint='max_norm',
bias_constraint='max_norm', activation='relu', input_shape=(16, 16, 10)))
model.build()
self.keras_param_test(model, 1, 14)
# ********** Recurrent Layers **********
Example 8
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 6 votes |
|
def test_keras_import(self):
model = Sequential()
model.add(LSTM(64, return_sequences=True, input_shape=(10, 64)))
model.add(SimpleRNN(32, return_sequences=True))
model.add(GRU(10, kernel_regularizer=regularizers.l2(0.01),
bias_regularizer=regularizers.l2(0.01), recurrent_regularizer=regularizers.l2(0.01),
activity_regularizer=regularizers.l2(0.01), kernel_constraint='max_norm',
bias_constraint='max_norm', recurrent_constraint='max_norm'))
model.build()
json_string = Model.to_json(model)
with open(os.path.join(settings.BASE_DIR, 'media', 'test.json'), 'w') as out:
json.dump(json.loads(json_string), out, indent=4)
sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.json'), 'r')
response = self.client.post(reverse('keras-import'), {'file': sample_file})
response = json.loads(response.content)
layerId = sorted(response['net'].keys())
self.assertEqual(response['result'], 'success')
self.assertGreaterEqual(len(response['net'][layerId[1]]['params']), 7)
self.assertGreaterEqual(len(response['net'][layerId[3]]['params']), 7)
self.assertGreaterEqual(len(response['net'][layerId[6]]['params']), 7)
# ********** Embedding Layers **********
Example 9
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 6 votes |
|
def test_keras_import(self):
model = Sequential()
model.add(BatchNormalization(center=True, scale=True, beta_regularizer=regularizers.l2(0.01),
gamma_regularizer=regularizers.l2(0.01),
beta_constraint='max_norm', gamma_constraint='max_norm',
input_shape=(16, 10)))
model.build()
json_string = Model.to_json(model)
with open(os.path.join(settings.BASE_DIR, 'media', 'test.json'), 'w') as out:
json.dump(json.loads(json_string), out, indent=4)
sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.json'), 'r')
response = self.client.post(reverse('keras-import'), {'file': sample_file})
response = json.loads(response.content)
layerId = sorted(response['net'].keys())
self.assertEqual(response['result'], 'success')
self.assertEqual(response['net'][layerId[0]]['info']['type'], 'Scale')
self.assertEqual(response['net'][layerId[1]]['info']['type'], 'BatchNorm')
# ********** Noise Layers **********
Example 10
| Project: oslodatascience-rl Author: Froskekongen File: erlenda_pong_parallel.py MIT License | 6 votes |
|
def buildmodel(opt):
print("Now we build the model")
model = Sequential()
model.add(Convolution2D(32, 8, 8, subsample=(4,4), border_mode='same',input_shape=(80,80,1)))
model.add(Activation('relu'))
model.add(Convolution2D(64, 4, 4, subsample=(2,2), border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, subsample=(1,1), border_mode='same'))
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dense(1))
model.compile(loss='binary_crossentropy',optimizer=opt)
print("We finish building the model")
return model
Example 11
| Project: deep-nn-car Author: scope-lab-vu File: train.py MIT License | 6 votes |
|
def createModel():
model = Sequential()
input1= Input(shape=(66,200,3), name='image')
input2=Input(shape=(1,), name='speed')
steer_inp = BatchNormalization(epsilon=0.001, axis=-1,momentum=0.99)(input1)
layer1 = Conv2D(24, (5, 5), padding="valid", strides=(2, 2), activation="relu")(steer_inp)
layer2 = Conv2D(36, (5, 5), padding="valid", strides=(2, 2), activation="relu")(layer1)
layer3 = Conv2D(48, (5, 5), padding="valid", strides=(2, 2), activation="relu")(layer2)
layer4 = Conv2D(64, (3, 3), padding="valid", strides=(1, 1), activation="relu")(layer3)
layer5 = Conv2D(64, (3, 3), padding="valid", strides=(1, 1), activation="relu")(layer4)
layer6 = Flatten()(layer5)
layer7 = Dense(1164, activation='relu')(layer6)
layer8 = Dense(100, activation='relu')(layer7)
layer9 = Dense(100, activation= 'relu')(input2)
merged=add([layer8, layer9])
layer10 = Dense(50, activation='relu')(merged)
layer11 = Dense(50, activation='relu')(layer10)
layer12 = Dense(10, activation='relu')(layer11)
steer_out = Dense(1, activation='tanh')(layer12)
model=Model(inputs=[input1,input2], outputs=steer_out)
return model
Example 12
| Project: ANN Author: waynezv File: ANN_v0.1.py MIT License | 6 votes |
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def train(in_dim, out_dim, X_train, Y_train, X_test, Y_test):
model = Sequential()
model.add(Dense(100000, input_dim = in_dim, init='uniform'))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(100000, init='uniform'))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(out_dim, init='uniform'))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd',\
metrics=['accuracy'])
hist = model.fit(X_train, Y_train, nb_epoch=5, batch_size=32,\
validation_split=0.1, shuffle=True)
print(hist.history)
loss_and_metrics = model.evaluate(X_test, Y_test, batch_size=32)
classes = model.predict_classes(X_test, batch_size=32)
proba = model.predict_proba(X_test, batch_size=32)
Example 13
| Project: Mnist-practise Author: hsmyy File: mnist.py MIT License | 6 votes |
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def CNN2(X_train, Y_train, X_test, Y_test, activation='relu'):
batch_size = 64
nb_classes = 10
nb_epoch = 20
np.random.seed(1337)
X_train = X_train.reshape(60000,1, 28, 28)
X_test = X_test.reshape(10000,1, 28, 28)
X_train = X_train.astype("float32")
X_test.astype("float32")
#X_train /= 255
#X_test /= 255
print(X_train.shape, 'train samples')
print(Y_train.shape, 'train labels')
print(X_test.shape, 'test smaples')
Y_train = np_utils.to_categorical(Y_train, nb_classes)
Y_test = np_utils.to_categorical(Y_test, nb_classes)
model = Sequential()
model.add(Convolution2D(4, 1, 5, 5, border_mode='valid'))
model.add(Activation(activation))
model.add(Convolution2D(8, 4, 3, 3, border_mode='valid'))
model.add(Activation(activation))
model.add(MaxPooling2D(poolsize=(2, 2)))
model.add(Convolution2D(16, 8, 3, 3, border_mode='valid'))
model.add(Activation(activation))
model.add(MaxPooling2D(poolsize=(2,2)))
model.add(Flatten())
model.add(Dense(16 * 4 * 4, 128, init='normal'))
model.add(Activation(activation))
model.add(Dense(128, nb_classes, init='normal'))
model.add(Activation('softmax'))
sgd = SGD(l2=0.0, lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=10, shuffle=True, verbose=1, show_accuracy=True, validation_split=0.2)
score = model.evaluate(X_test, Y_test, batch_size=batch_size)
print('Test score:', score)
Example 14
| Project: Mnist-practise Author: hsmyy File: mnist.py MIT License | 6 votes |
|
def CNN(X_train, Y_train, X_test, Y_test):
batch_size = 64
nb_classes = 10
nb_epoch = 20
np.random.seed(1337)
X_train = X_train.reshape(60000,1, 28, 28)
X_test = X_test.reshape(10000,1, 28, 28)
X_train = X_train.astype("float32")
X_test.astype("float32")
#X_train /= 255
#X_test /= 255
print(X_train.shape, 'train samples')
print(Y_train.shape, 'train labels')
print(X_test.shape, 'test smaples')
Y_train = np_utils.to_categorical(Y_train, nb_classes)
Y_test = np_utils.to_categorical(Y_test, nb_classes)
model = Sequential()
model.add(Convolution2D(20, 1, 4, 4))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(MaxPooling2D(poolsize=(2, 2), ignore_border=False))
model.add(Convolution2D(40, 20, 5, 5))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(MaxPooling2D(poolsize=(3, 3)))
model.add(Flatten())
model.add(Dense(40 * 3 * 3, 150, init='normal'))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(150, nb_classes, init='normal'))
model.add(Activation('softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=30)
score = model.evaluate(X_test, Y_test, batch_size=batch_size)
print('Test score:', score)
Example 15
| Project: automlk Author: pierre-chaville File: keras_wrapper.py MIT License | 6 votes |
|
def keras_create_model(params, problem_type):
# creates a neural net model with params definition
log.info('creating NN structure')
model = Sequential()
for l in range(int(params['number_layers'])):
if l == 0:
model.add(Dense(units=params['units'], input_dim=params['input_dim']))
else:
model.add(Dense(units=params['units']))
model.add(Activation(params['activation']))
if params['batch_normalization']:
model.add(BatchNormalization())
model.add(Dropout(params['dropout']))
model.add(Dense(params['output_dim']))
if problem_type == 'classification':
model.add(Activation('sigmoid'))
keras_compile_model(model, params, problem_type)
return model
Example 16
| Project: Deep-Learning-for-HSI-classification Author: luozm File: cnn.py MIT License | 6 votes |
|
def cnn_2d(input_shape):
model = Sequential()
model.add(Conv2D(100, (3, 3), padding='valid', activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Conv2D(200, (3, 3), padding='valid', activation='relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Flatten())
model.add(Dense(200, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(84, activation='relu'))
model.add(Dense(nb_classes, activation='softmax'))
adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=['accuracy'])
return model
Example 17
| Project: Keras-GAN Author: eriklindernoren File: sgan.py MIT License | 6 votes |
|
def build_generator(self):
model = Sequential()
model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
model.add(Reshape((7, 7, 128)))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(1, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
model.summary()
noise = Input(shape=(self.latent_dim,))
img = model(noise)
return Model(noise, img)
Example 18
| Project: Keras-GAN Author: eriklindernoren File: context_encoder.py MIT License | 6 votes |
|
def build_discriminator(self):
model = Sequential()
model.add(Conv2D(64, kernel_size=3, strides=2, input_shape=self.missing_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(256, kernel_size=3, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
model.summary()
img = Input(shape=self.missing_shape)
validity = model(img)
return Model(img, validity)
Example 19
| Project: Keras-GAN Author: eriklindernoren File: ccgan.py MIT License | 6 votes |
|
def build_discriminator(self):
img = Input(shape=self.img_shape)
model = Sequential()
model.add(Conv2D(64, kernel_size=4, strides=2, padding='same', input_shape=self.img_shape))
model.add(LeakyReLU(alpha=0.8))
model.add(Conv2D(128, kernel_size=4, strides=2, padding='same'))
model.add(LeakyReLU(alpha=0.2))
model.add(InstanceNormalization())
model.add(Conv2D(256, kernel_size=4, strides=2, padding='same'))
model.add(LeakyReLU(alpha=0.2))
model.add(InstanceNormalization())
model.summary()
img = Input(shape=self.img_shape)
features = model(img)
validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(features)
label = Flatten()(features)
label = Dense(self.num_classes+1, activation="softmax")(label)
return Model(img, [validity, label])
Example 20
| Project: Keras-GAN Author: eriklindernoren File: bigan.py MIT License | 6 votes |
|
def build_generator(self):
model = Sequential()
model.add(Dense(512, input_dim=self.latent_dim))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(np.prod(self.img_shape), activation='tanh'))
model.add(Reshape(self.img_shape))
model.summary()
z = Input(shape=(self.latent_dim,))
gen_img = model(z)
return Model(z, gen_img)
Example 21
| Project: sanctuary Author: bzamecnik File: mnist.py MIT License | 5 votes |
|
def create_conv_model(input_shape, nb_classes, nb_conv_blocks,
nb_filters, pool_size, kernel_size, dropout, activation,
fc_layer_width):
model = Sequential()
nb_rows, nb_cols = input_shape
# add one dimension for the convolution filters
model.add(Reshape((nb_rows, nb_cols, 1), input_shape=input_shape))
for i in range(nb_conv_blocks):
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
border_mode='valid'))
model.add(Activation(activation))
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
model.add(Activation(activation))
model.add(MaxPooling2D(pool_size=pool_size))
if dropout > 0:
model.add(Dropout(dropout))
model.add(Flatten())
model.add(Dense(fc_layer_width))
model.add(Activation(activation))
model.add(Dropout(dropout))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
Example 22
| Project: sklearn2docker Author: KhaledSharif File: keras_classifier_test.py GNU Lesser General Public License v3.0 | 5 votes |
|
def create_binary_classification_model():
from keras.models import Sequential
from keras.layers import Dense, Dropout
model = Sequential()
model.add(Dense(64, input_shape=(30,), activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam')
return model
Example 23
| Project: sklearn2docker Author: KhaledSharif File: keras_classifier_test.py GNU Lesser General Public License v3.0 | 5 votes |
|
def create_categorical_classification_model():
from keras.models import Sequential
from keras.layers import Dense, Dropout
model = Sequential()
model.add(Dense(64, input_shape=(30,), activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(3, activation='sigmoid'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
return model
Example 24
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 5 votes |
|
def test_keras_import(self):
model = Sequential()
model.add(Dense(100, kernel_regularizer=regularizers.l2(0.01), bias_regularizer=regularizers.l2(0.01),
activity_regularizer=regularizers.l2(0.01), kernel_constraint='max_norm',
bias_constraint='max_norm', activation='relu', input_shape=(16,)))
model.build()
self.keras_param_test(model, 1, 3)
Example 25
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 5 votes |
|
def test_keras_import(self):
model = Sequential()
model.add(Dropout(0.5, input_shape=(64, 10)))
model.build()
self.keras_type_test(model, 0, 'Dropout')
Example 26
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 5 votes |
|
def test_keras_import(self):
model = Sequential()
model.add(Flatten(input_shape=(64, 10)))
model.build()
self.keras_type_test(model, 0, 'Flatten')
Example 27
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 5 votes |
|
def test_keras_import(self):
model = Sequential()
model.add(Reshape((5, 2), input_shape=(10,)))
model.build()
self.keras_type_test(model, 0, 'Reshape')
Example 28
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 5 votes |
|
def test_keras_import(self):
model = Sequential()
model.add(Permute((2, 1), input_shape=(64, 10)))
model.build()
self.keras_type_test(model, 0, 'Permute')
Example 29
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 5 votes |
|
def test_keras_import(self):
model = Sequential()
model.add(ActivityRegularization(l1=2, input_shape=(10,)))
model.build()
self.keras_type_test(model, 0, 'Regularization')
Example 30
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 5 votes |
|
def test_keras_import(self):
model = Sequential()
model.add(Masking(mask_value=0., input_shape=(100, 5)))
model.build()
self.keras_type_test(model, 0, 'Masking')
# ********** Convolutional Layers **********
Example 31
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 5 votes |
|
def test_keras_import(self):
model = Sequential()
model.add(SeparableConv2D(32, 3, depthwise_regularizer=regularizers.l2(0.01),
pointwise_regularizer=regularizers.l2(0.01),
bias_regularizer=regularizers.l2(0.01),
activity_regularizer=regularizers.l2(0.01), depthwise_constraint='max_norm',
bias_constraint='max_norm', pointwise_constraint='max_norm',
activation='relu', input_shape=(16, 16, 1)))
self.keras_param_test(model, 1, 12)
Example 32
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 5 votes |
|
def test_keras_import(self):
model = Sequential()
model.add(Conv2DTranspose(32, (3, 3), kernel_regularizer=regularizers.l2(0.01),
bias_regularizer=regularizers.l2(0.01),
activity_regularizer=regularizers.l2(0.01), kernel_constraint='max_norm',
bias_constraint='max_norm', activation='relu', input_shape=(16, 16, 1)))
model.build()
self.keras_param_test(model, 1, 13)
Example 33
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 5 votes |
|
def test_keras_import(self):
# Global Pooling 1D
model = Sequential()
model.add(GlobalMaxPooling1D(input_shape=(16, 1)))
model.build()
self.keras_param_test(model, 0, 5)
# Global Pooling 2D
model = Sequential()
model.add(GlobalMaxPooling2D(input_shape=(16, 16, 1)))
model.build()
self.keras_param_test(model, 0, 8)
# Pooling 1D
model = Sequential()
model.add(MaxPooling1D(pool_size=2, strides=2, padding='same', input_shape=(16, 1)))
model.build()
self.keras_param_test(model, 0, 5)
# Pooling 2D
model = Sequential()
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', input_shape=(16, 16, 1)))
model.build()
self.keras_param_test(model, 0, 8)
# Pooling 3D
model = Sequential()
model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), padding='same',
input_shape=(16, 16, 16, 1)))
model.build()
self.keras_param_test(model, 0, 11)
# ********** Locally-connected Layers **********
Example 34
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 5 votes |
|
def test_keras_import(self):
model = Sequential()
model.add(Embedding(1000, 64, input_length=10, embeddings_regularizer=regularizers.l2(0.01),
embeddings_constraint='max_norm'))
model.build()
self.keras_param_test(model, 0, 7)
# ********** Merge Layers **********
Example 35
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 5 votes |
|
def test_keras_import(self):
model = Sequential()
model.add(GaussianDropout(rate=0.5, input_shape=(16, 1)))
model.build()
self.keras_param_test(model, 0, 1)
Example 36
| Project: VisualNN Author: angelhunt File: test_views.py GNU General Public License v3.0 | 5 votes |
|
def test_keras_import(self):
model = Sequential()
model.add(AlphaDropout(rate=0.5, seed=5, input_shape=(16, 1)))
model.build()
self.keras_param_test(model, 0, 1)
# ********** Utility Layers **********
Example 37
| Project: Kaggle-Statoil-Challenge Author: adodd202 File: capsulenet.py MIT License | 5 votes |
|
def CapsNet(input_shape, n_class, num_routing):
"""
A Capsule Network on MNIST.
:param input_shape: data shape, 3d, [width, height, channels]
:param n_class: number of classes
:param num_routing: number of routing iterations
:return: Two Keras Models, the first one used for training, and the second one for evaluation.
`eval_model` can also be used for training.
"""
x = layers.Input(shape=input_shape)
# Layer 1: Just a conventional Conv2D layer
conv1 = layers.Conv2D(filters=256, kernel_size=9, strides=1, padding='valid', activation='relu', name='conv1')(x)
# Layer 2: Conv2D layer with `squash` activation, then reshape to [None, num_capsule, dim_capsule]
primarycaps = PrimaryCap(conv1, dim_capsule=8, n_channels=32, kernel_size=9, strides=2, padding='valid')
# Layer 3: Capsule layer. Routing algorithm works here.
digitcaps = CapsuleLayer(num_capsule=n_class, dim_capsule=16, num_routing=num_routing,
name='digitcaps')(primarycaps)
# Layer 4: This is an auxiliary layer to replace each capsule with its length. Just to match the true label's shape.
# If using tensorflow, this will not be necessary. :)
out_caps = Length(name='capsnet')(digitcaps)
# Decoder network.
y = layers.Input(shape=(n_class,))
masked_by_y = Mask()([digitcaps, y]) # The true label is used to mask the output of capsule layer. For training
masked = Mask()(digitcaps) # Mask using the capsule with maximal length. For prediction
# Shared Decoder model in training and prediction
decoder = models.Sequential(name='decoder')
decoder.add(layers.Dense(512, activation='relu', input_dim=16*n_class))
decoder.add(layers.Dense(1024, activation='relu'))
decoder.add(layers.Dense(np.prod(input_shape), activation='sigmoid'))
decoder.add(layers.Reshape(target_shape=input_shape, name='out_recon'))
# Models for training and evaluation (prediction)
train_model = models.Model([x, y], [out_caps, decoder(masked_by_y)])
eval_model = models.Model(x, [out_caps, decoder(masked)])
return train_model, eval_model
Example 38
| Project: neural-fingerprinting Author: StephanZheng File: test_utils_keras.py BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def setUp(self):
from keras.models import Sequential
from keras.layers import Dense, Activation
import tensorflow as tf
def dummy_model():
input_shape = (100,)
return Sequential([Dense(20, name='l1',
input_shape=input_shape),
Dense(10, name='l2'),
Activation('softmax', name='softmax')])
self.sess = tf.Session()
self.sess.as_default()
self.model = dummy_model()
Example 39
| Project: deep-learning-note Author: wdxtub File: 3_price_prediction.py MIT License | 5 votes |
|
def build_model():
model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(train_data.shape[1], )))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(1))
model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
return model
Example 40
| Project: keras-anomaly-detection Author: chen0040 File: convolutional.py MIT License | 5 votes |
|
def create_model(time_window_size, metric):
model = Sequential()
model.add(Conv1D(filters=256, kernel_size=5, padding='same', activation='relu',
input_shape=(time_window_size, 1)))
model.add(GlobalMaxPool1D())
model.add(Dense(units=time_window_size, activation='linear'))
model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric])
print(model.summary())
return model
Example 41
| Project: keras-anomaly-detection Author: chen0040 File: recurrent.py MIT License | 5 votes |
|
def create_model(time_window_size, metric):
model = Sequential()
model.add(LSTM(units=128, input_shape=(time_window_size, 1), return_sequences=False))
model.add(Dense(units=time_window_size, activation='linear'))
model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric])
print(model.summary())
return model
Example 42
| Project: keras-anomaly-detection Author: chen0040 File: recurrent.py MIT License | 5 votes |
|
def create_model(time_window_size, metric):
model = Sequential()
model.add(Bidirectional(LSTM(units=64, dropout=0.2, recurrent_dropout=0.2), input_shape=(time_window_size, 1)))
model.add(Dense(units=time_window_size, activation='linear'))
model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric])
# model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric])
# model.compile(optimizer="sgd", loss="mse", metrics=[metric])
print(model.summary())
return model
Example 43
| Project: Handwritten-Digit-Recognition-using-Deep-Learning Author: anujdutt9 File: neural_network.py MIT License | 5 votes |
|
def build(width, height, depth, total_classes, Saved_Weights_Path=None):
# Initialize the Model
model = Sequential()
# First CONV => RELU => POOL Layer
model.add(Conv2D(20, 5, 5, border_mode="same", input_shape=(depth, height, width)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), dim_ordering="th"))
# Second CONV => RELU => POOL Layer
model.add(Conv2D(50, 5, 5, border_mode="same"))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), dim_ordering="th"))
# Third CONV => RELU => POOL Layer
# Convolution -> ReLU Activation Function -> Pooling Layer
model.add(Conv2D(100, 5, 5, border_mode="same"))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), dim_ordering="th"))
# FC => RELU layers
# Fully Connected Layer -> ReLU Activation Function
model.add(Flatten())
model.add(Dense(500))
model.add(Activation("relu"))
# Using Softmax Classifier for Linear Classification
model.add(Dense(total_classes))
model.add(Activation("softmax"))
# If the saved_weights file is already present i.e model is pre-trained, load that weights
if Saved_Weights_Path is not None:
model.load_weights(Saved_Weights_Path)
return model
# --------------------------------- EOC ------------------------------------
Example 44
| Project: Mnist-practise Author: hsmyy File: mnist.py MIT License | 5 votes |
|
def DNN(X_train, Y_train, X_test, Y_test):
batch_size = 64
nb_classes = 10
nb_epoch = 20
np.random.seed(1337)
X_train = X_train.reshape(60000,784)
X_test = X_test.reshape(10000,784)
X_train = X_train.astype("float32")
X_test.astype("float32")
X_train /= 255
X_test /= 255
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test smaples')
Y_train = np_utils.to_categorical(Y_train, nb_classes)
Y_test = np_utils.to_categorical(Y_test, nb_classes)
model = Sequential()
model.add(Dense(784, 128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(128,128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(128,10))
model.add(Activation('softmax'))
rms = RMSprop()
model.compile(loss='categorical_crossentropy', optimizer=rms)
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=2, validation_data=(X_test, Y_test))
score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
Example 45
| Project: dac Author: KBNLresearch File: models.py GNU General Public License v3.0 | 5 votes |
|
def create_model(self):
'''
Create new keras model.
'''
self.class_weight = {0: 0.25, 1: 0.75}
model = Sequential()
model.add(Dense(self.data.shape[1], input_dim=self.data.shape[1],
activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='RMSprop', loss='binary_crossentropy',
metrics=['accuracy'])
return model
Example 46
| Project: wrangle Author: autonomio File: create_synth_model.py MIT License | 5 votes |
|
def _base_for_model(mode, n=50, neurons=50):
from keras.models import Sequential
from keras.layers import Dense
from .create_synth_data import create_synth_data
x, y = create_synth_data(mode, n=n)
model = Sequential()
model.add(Dense(neurons,
input_dim=x.shape[1],
activation='relu'))
return x, y, model
Example 47
| Project: RFMLS-NEU Author: neu-spiral File: BaselineModel2D.py MIT License | 5 votes |
|
def getBaselineModel2D(slice_size=64, classes=1000, cnn_stacks=3, fc_stacks=1, channels=128, dropout_flag=True, \
fc1=256, fc2=128, batchnorm=False, \
#optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False), \
loss='categorical_crossentropy'):
model = models.Sequential()
model.add(Conv2D(channels,(7, 2),activation='relu', padding='same', input_shape=(slice_size, 2, 1)))
model.add(Conv2D(channels,(5, 2),activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 1)))
model.add(Conv2D(channels,(7, 2),activation='relu', padding='same'))
model.add(Conv2D(channels,(5, 2),activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 1)))
model.add(Conv2D(channels,(7, 2),activation='relu', padding='same'))
model.add(Conv2D(channels,(5, 2),activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 1)))
model.add(Flatten())
model.add(Dense(fc1, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(fc2, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(classes, activation='softmax'))
# optimizer = Adam(lr=LR, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
# model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
model.summary()
return model
Example 48
| Project: RFMLS-NEU Author: neu-spiral File: HomegrownModel.py MIT License | 5 votes |
|
def getHomegrownModel(slice_size=1024, classes=1000, cnn_stacks=3, fc_stacks=1, channels=128, dropout_flag=True, \
flt=[50, 50, 256, 80],k1=[1, 7], k2=[2, 7], batchnorm=False, dr=0.5,\
#optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False), \
loss='categorical_crossentropy'):
"""Original Homegrown model"""
in_shp = [2, slice_size]
model_nn = models.Sequential()
model_nn.add(Reshape([1] + in_shp, input_shape=in_shp))
model_nn.add(ZeroPadding2D((0, 2)))
model_nn.add(Conv2D(flt[0], (k1[0], k1[1]), padding="valid", kernel_initializer="glorot_uniform", name="conv1"))
if batchnorm:
model_nn.add(keras.layers.BatchNormalization(momentum=0.9, name='bn_1'))
model_nn.add(Activation('relu'))
model_nn.add(ZeroPadding2D((0, 2)))
model_nn.add(Conv2D(flt[1], (k2[0], k2[1]), padding="valid", kernel_initializer="glorot_uniform", name="conv2"))
if batchnorm:
model_nn.add(keras.layers.BatchNormalization(momentum=0.9, name='bn_2'))
model_nn.add(Activation('relu'))
model_nn.add(Flatten())
model_nn.add(Dense(flt[2], kernel_initializer='he_normal', name="dense1"))
if batchnorm:
model_nn.add(keras.layers.BatchNormalization(momentum=0.9, name='bn_3'))
model_nn.add(Activation('relu'))
model_nn.add(Dropout(dr))
model_nn.add(Dense(flt[3], kernel_initializer='he_normal', name="dense2"))
if batchnorm:
model_nn.add(keras.layers.BatchNormalization(momentum=0.9, name='bn_4'))
model_nn.add(Activation('relu'))
model_nn.add(Dropout(dr))
model_nn.add(Dense(classes, kernel_initializer='he_normal', kernel_regularizer=l2(0.0001), name="dense3"))
model_nn.add(Activation('softmax'))
model_nn.summary()
return model_nn
Example 49
| Project: RFMLS-NEU Author: neu-spiral File: BaselineModel.py MIT License | 5 votes |
|
def getBaselineModel(slice_size=64, classes=1000, cnn_stacks=3, fc_stacks=1, channels=128, dropout_flag=True, \
fc1=256, fc2=128, batchnorm=False, \
#optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False), \
loss='categorical_crossentropy'):
"""A dummy model to test the functionalities of the Data Generator"""
model = models.Sequential()
model.add(Conv1D(channels,7,activation='relu', padding='same', input_shape=(slice_size, 2)))
model.add(Conv1D(channels,5, padding='same'))
if batchnorm:
model.add(keras.layers.BatchNormalization(momentum=0.9, name='bn_1'))
model.add(Activation('relu'))
model.add(MaxPooling1D())
for i in range(1, cnn_stacks):
model.add(Conv1D(channels,7,activation='relu', padding='same'))
model.add(Conv1D(channels,5, padding='same'))
if batchnorm:
model.add(keras.layers.BatchNormalization(momentum=0.9, name='bn_'+str(i+1)))
model.add(Activation('relu'))
model.add(MaxPooling1D())
#model.add(Conv1D(128,7,activation='relu', padding='same'))
#model.add(Conv1D(128,5,activation='relu', padding='same'))
#model.add(MaxPooling1D())
model.add(Flatten())
for j in range(1, fc_stacks):
model.add(Dense(fc1, activation='relu'))
if dropout_flag:
model.add(Dropout(0.5))
model.add(Dense(fc2, activation='relu'))
if dropout_flag:
model.add(Dropout(0.5))
model.add(Dense(classes, activation='softmax'))
#optimizer = optimizer
#optimizer = Adam(lr=lr, beta_1=beta_2, beta_2=beta_2, epsilon=epsilon, decay=decay, amsgrad=amsgrad)
#model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy'])
#model.summary()
return model
Example 50
| Project: RFMLS-NEU Author: neu-spiral File: evaluate_model.py MIT License | 5 votes |
|
def getModel(slice_size, classes):
"""A dummy model to test the functionalities of the Data Generator"""
model = models.Sequential()
model.add(Conv1D(args.num_filters,7,activation='relu', padding='same', input_shape=(slice_size, 2)))
model.add(Conv1D(args.num_filters,5,activation='relu', padding='same'))
model.add(MaxPooling1D())
model.add(Conv1D(args.num_filters,7,activation='relu', padding='same'))
model.add(Conv1D(args.num_filters,5,activation='relu', padding='same'))
model.add(MaxPooling1D())
model.add(Conv1D(args.num_filters,7,activation='relu', padding='same'))
model.add(Conv1D(args.num_filters,5,activation='relu', padding='same'))
model.add(MaxPooling1D())
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(classes, activation='softmax'))
optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
model.summary()
return model
# Take one random 1k dataset just to test the Data Generator
Example 51
| Project: Stock_Market_Forecast Author: cuevas1208 File: keras_model.py MIT License | 5 votes |
|
def __init__(self):
#self.model = Sequential()
self.get_model()
Example 52
| Project: Stock_Market_Forecast Author: cuevas1208 File: keras_model.py MIT License | 5 votes |
|
def get_model(self):
self.model = Sequential()
# Preprocess incoming data
# Normalize the features using Min-Max scaling centered around zero and reshape
self.model.add(Lambda(lambda x: (x/6) - 1., input_shape=(32, 32, 1), output_shape=(32, 32, 1)))
print(self.model.output_shape)
#self.model.add(Dense(32, input_shape=(32, 32, 1)))
self.model.add(Convolution2D(4, 2, 2))
print(self.model.output_shape)
self.model.add(AveragePooling2D((2, 2)))
self.model.add(Activation('relu'))
print(self.model.output_shape)
self.model.add(Convolution2D(8, 1, 1))
self.model.add(Activation('relu'))
print(self.model.output_shape)
self.model.add(Convolution2D(16, 2, 2))
print(self.model.output_shape)
self.model.add(AveragePooling2D((2, 2)))
self.model.add(Activation('relu'))
print(self.model.output_shape)
self.model.add(Flatten())
self.model.add(Activation('relu'))
print(self.model.output_shape)
self.model.add(Dense(200))
self.model.add(Activation('relu'))
classes = getclassesTotal()
self.model.add(Dense(classes))
if (classes > 1):
model.add(Dense(classes, activation='softmax'))
self.model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
else:
self.model.compile(optimizer="adam", loss="mse", metrics=['accuracy'])
print(self.model.output_shape)
Example 53
| Project: ppi_lstm_rnn_keras Author: ylhsieh File: train_keras.py MIT License | 5 votes |
|
def main():
def build_model():
model = Sequential()
model.add(Embedding(len(train_vocab), hidden_size, weights=[embedding_array],\
input_length=max_sequence_length))
model.add(Dropout(dropout_rate))
model.add(Bidirectional(LSTM(rnn_hidden_size)))
model.add(Dropout(dropout_rate))
model.add(Dense(2, activation='softmax'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
return model
train_vocab = load_vocab_from(opt.data + '.vocab')
embedding_array = load_pretrained_embeddings(train_vocab, pretrained_embeddings_file)
for fold_id in range(10):
tfsession = tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.5)))
K.set_session(tfsession)
train_file = 'corpus/{}_f{}_train.txt'.format(opt.data, fold_id)
test_file = 'corpus/{}_f{}_test.txt'.format(opt.data, fold_id)
log_file = '{}_f{}.log'.format(opt.data, fold_id)
x_train, x_test, y_train, y_test, _ = read_corpus(train_file, test_file, train_vocab)
fscore_cb = FscoreLogCallback(log_file)
model = build_model()
print("Fold {}".format(fold_id))
model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size, \
callbacks=[fscore_cb], verbose=2)
predicted = np.argmax(model.predict(x_test), axis=1)
y_test_to_label = np.argmax(y_test, axis=1)
prec, reca, fscore, sup = precision_recall_fscore_support(y_test_to_label, predicted, average='binary')
print("Final Precision:{:2.2f}% Recall:{:2.2f}% Fscore:{:2.2f}%".format(prec*100, reca*100, fscore*100))
Example 54
| Project: Anamoly-Detection Author: msmsk05 File: auto_encoder.py BSD 2-Clause "Simplified" License | 5 votes |
|
def _build_model(self):
model = Sequential()
# Input layer
model.add(Dense(
self.hidden_neurons_[0], activation=self.hidden_activation,
input_shape=(self.n_features_,),
activity_regularizer=l2(self.l2_regularizer)))
model.add(Dropout(self.dropout_rate))
# Additional layers
for i, hidden_neurons in enumerate(self.hidden_neurons_, 1):
model.add(Dense(
hidden_neurons,
activation=self.hidden_activation,
activity_regularizer=l2(self.l2_regularizer)))
model.add(Dropout(self.dropout_rate))
# Output layers
model.add(Dense(self.n_features_, activation=self.output_activation,
activity_regularizer=l2(self.l2_regularizer)))
# Compile model
model.compile(loss=self.loss, optimizer=self.optimizer)
print(model.summary())
return model
# noinspection PyUnresolvedReferences
Example 55
| Project: Anamoly-Detection Author: msmsk05 File: gaal_base.py BSD 2-Clause "Simplified" License | 5 votes |
|
def create_discriminator(latent_size, data_size): # pragma: no cover
"""Create the discriminator of the GAN for a given latent size.
Parameters
----------
latent_size : int
The size of the latent space of the generator.
data_size : int
Size of the input data.
Returns
-------
D : Keras model() object
Returns a model() object.
"""
dis = Sequential()
dis.add(Dense(int(math.ceil(math.sqrt(data_size))),
input_dim=latent_size, activation='relu',
kernel_initializer=keras.initializers.VarianceScaling(
scale=1.0, mode='fan_in', distribution='normal',
seed=None)))
dis.add(Dense(1, activation='sigmoid',
kernel_initializer=keras.initializers.VarianceScaling(
scale=1.0, mode='fan_in', distribution='normal',
seed=None)))
data = Input(shape=(latent_size,))
fake = dis(data)
return Model(data, fake)
Example 56
| Project: AIGame Author: chenghongkuan File: RL_brain_Keras.py GNU General Public License v3.0 | 5 votes |
|
def _build_net(self):
# ------------------ 建造估计层 ------------------
# 因为神经网络在这个地方只是用来输出不同动作对应的Q值，最后的决策是用Q表的选择来做的
# 所以其实这里的神经网络可以看做是一个线性的，也就是通过不同的输入有不同的输出，而不是确定类别的几个输出
# 这里我们先按照上一个例子造一个两层每层单个神经元的神经网络
self.model_eval = Sequential([
# 输入 并且给每一个神经元配一个激活函数
Dense(self.first_layer_neurno, input_dim=self.n_features, activation='relu'),
# Activation('relu'),
# Dense(1, activation='tanh'),
# 输出
Dense(self.n_actions),
])
# 选择rms优化器，输入学习率参数
rmsprop = RMSprop(lr=self.lr, rho=0.9, epsilon=1e-08, decay=0.0)
self.model_eval.compile(loss='mse',
optimizer=rmsprop,
metrics=['accuracy'])
# ------------------ 构建目标神经网络 ------------------
# 目标神经网络的架构必须和估计神经网络一样，但是不需要计算损失函数
self.model_target = Sequential([
# 输入 并且给每一个神经元配一个激活函数
Dense(self.first_layer_neurno, input_dim=self.n_features, activation='relu'),
# Activation('relu'),
# Dense(1, activation='tanh'),
# 输出
Dense(self.n_actions),
])
Example 57
| Project: DeepLearningMugenKnock Author: yoyoyo-yo File: gan_keras.py MIT License | 5 votes |
|
def Combined_model(g, d):
model = Sequential()
model.add(g)
model.add(d)
return model
Example 58
| Project: DeepLearningMugenKnock Author: yoyoyo-yo File: cgan_cifar10_keras.py MIT License | 5 votes |
|
def Combined_model(g, d):
inputs = Input([100, ], name="x")
con_x = Input([num_classes, ], name="con_x")
con_x2 = Input([img_height, img_width, num_classes], name="con_x2")
x = g(inputs=[inputs, con_x, con_x2])
x = d(x)
model = Model(inputs=[inputs, con_x, con_x2], outputs=[x])
#model = Sequential()
#model.add(g)
#model.add(d)
return model
Example 59
| Project: DeepLearningMugenKnock Author: yoyoyo-yo File: dcgan_keras.py MIT License | 5 votes |
|
def Combined_model(g, d):
model = Sequential()
model.add(g)
model.add(d)
return model
Example 60
| Project: DeepLearningMugenKnock Author: yoyoyo-yo File: gan_cifar10_keras.py MIT License | 5 votes |
|
def Combined_model(g, d):
model = Sequential()
model.add(g)
model.add(d)
return model
Example 61
| Project: DeepLearningMugenKnock Author: yoyoyo-yo File: cgan_mnist_keras.py MIT License | 5 votes |
|
def Combined_model(g, d):
inputs = Input([100, ], name="x")
con_x = Input([num_classes, ], name="con_x")
con_x2 = Input([img_height, img_width, num_classes], name="con_x2")
x = g(inputs=[inputs, con_x, con_x2])
x = d(x)
model = Model(inputs=[inputs, con_x, con_x2], outputs=[x])
#model = Sequential()
#model.add(g)
#model.add(d)
return model
Example 62
| Project: Keras-GAN Author: eriklindernoren File: sgan.py MIT License | 5 votes |
|
def build_discriminator(self):
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Flatten())
model.summary()
img = Input(shape=self.img_shape)
features = model(img)
valid = Dense(1, activation="sigmoid")(features)
label = Dense(self.num_classes+1, activation="softmax")(features)
return Model(img, [valid, label])
Example 63
| Project: Keras-GAN Author: eriklindernoren File: context_encoder.py MIT License | 5 votes |
|
def build_generator(self):
model = Sequential()
# Encoder
model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(512, kernel_size=1, strides=2, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.5))
# Decoder
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(Activation('relu'))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(Activation('relu'))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(self.channels, kernel_size=3, padding="same"))
model.add(Activation('tanh'))
model.summary()
masked_img = Input(shape=self.img_shape)
gen_missing = model(masked_img)
return Model(masked_img, gen_missing)
Example 64
| Project: MODS_ConvNet Author: santiagolopezg File: little_foo3.py MIT License | 4 votes |
|
def foo():
# Determine proper input shape
if keras.__version__ > '1.0.3':
K.set_image_dim_ordering('th')
input_shape = (1, 224, 224)
#img_input = Input(shape=input_shape)
model = Sequential()
model.add(Convolution2D(32, 8, 8,
input_shape=input_shape,init=weight_init, name='conv1_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(32, 6, 6,init=weight_init, name='conv1_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(32, 4, 4,init=weight_init, name='conv1_3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(32, 2, 2,init=weight_init, name='conv1_4'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2))) # in 208, out 104
model.add(Dropout(dropout))
model.add(Convolution2D(64, 8, 8,init=weight_init, name='conv2_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(64, 6, 6,init=weight_init, name='conv2_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(64, 4, 4,init=weight_init, name='conv2_3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(64, 2, 2,init=weight_init, name='conv2_4'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2))) # in is 88, out is 44
model.add(Dropout(dropout))
model.add(Flatten())
model.add(Dense(220, init=weight_init))
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(Dense(2))
model.add(Activation('sigmoid'))
return model
Example 65
| Project: MODS_ConvNet Author: santiagolopezg File: foo_three.py MIT License | 4 votes |
|
def foo():
# Determine proper input shape
if keras.__version__ > '1.0.3':
K.set_image_dim_ordering('th')
input_shape = (1, 224, 224)
#img_input = Input(shape=input_shape)
model = Sequential()
model.add(Convolution2D(32, 3, 3,
input_shape=input_shape,init=weight_init, name='conv1_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3,init=weight_init, name='conv1_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(64, 3, 3,init=weight_init, name='conv2_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3,init=weight_init, name='conv2_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3,init=weight_init, name='conv2_3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(128, 3, 3,init=weight_init, name='conv3_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(128, 3, 3,init=weight_init, name='conv3_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(512, 3,3,init=weight_init, border_mode='same', name='conv4_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(512, 3,3,init=weight_init, border_mode='same', name='conv4_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Flatten())
model.add(Dense(120, init=weight_init))
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(Dropout(dropout))
model.add(Dense(2))
model.add(Activation('sigmoid'))
return model
Example 66
| Project: MODS_ConvNet Author: santiagolopezg File: convnet_keras_1.py MIT License | 4 votes |
|
def network(regl1, regl2, weight_init, dropout, optimize):
#create network architecture
model = Sequential()
model.add(Convolution2D(16, 5, 5,input_shape=(1, 256, 192),W_regularizer=l1l2(l1=regl1, l2=regl2),init=weight_init))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(32, 3, 3, W_regularizer=l1l2(l1=regl1, l2=regl2),init=weight_init))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(64, 3, 3, W_regularizer=l1l2(l1=regl1, l2=regl2),init=weight_init))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(64, 3, 3, W_regularizer=l1l2(l1=regl1, l2=regl2),init=weight_init))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(64, 3, 3, W_regularizer=l1l2(l1=regl1, l2=regl2),init=weight_init))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Flatten())
model.add(Dense(50,W_regularizer=l1l2(l1=regl1, l2=regl2),init=weight_init))
#model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(output_dim=1))
model.add(Activation('sigmoid'))
model.compile(optimizer=optimize, loss='binary_crossentropy', metrics=['accuracy'])
return model
Example 67
| Project: MODS_ConvNet Author: santiagolopezg File: little_foo.py MIT License | 4 votes |
|
def foo():
# Determine proper input shape
if keras.__version__ > '1.0.3':
K.set_image_dim_ordering('th')
input_shape = (1, 224, 224)
#img_input = Input(shape=input_shape)
model = Sequential()
model.add(Convolution2D(32, 5, 5,
input_shape=input_shape,init=weight_init, name='conv1_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3,init=weight_init, name='conv1_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3,init=weight_init, name='conv1_3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2))) # in 116, out 58
model.add(Dropout(dropout))
model.add(Convolution2D(64, 3, 3,init=weight_init, name='conv2_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3,init=weight_init, name='conv2_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3,init=weight_init, name='conv2_3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2))) # in is 52, out is 26
model.add(Dropout(dropout))
model.add(Convolution2D(128, 3, 3,init=weight_init, name='conv3_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(128, 3, 3,init=weight_init, name='conv3_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(128, 3, 3,init=weight_init, name='conv3_3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2))) # in is 20, out is 10
model.add(Dropout(dropout))
model.add(Flatten())
model.add(Dense(10, init=weight_init))
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(Dense(2))
model.add(Activation('sigmoid'))
return model
Example 68
| Project: MODS_ConvNet Author: santiagolopezg File: convnet_keras_2.py MIT License | 4 votes |
|
def network(regl1, regl2, weight_init, dropout, optimize):
#create network architecture
model = Sequential()
model.add(Convolution2D(16, 5, 5,input_shape=(1, 256, 192),W_regularizer=l1l2(l1=regl1, l2=regl2),init=weight_init))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(32, 3, 3, W_regularizer=l1l2(l1=regl1, l2=regl2),init=weight_init))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(64, 3, 3, W_regularizer=l1l2(l1=regl1, l2=regl2),init=weight_init))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(64, 3, 3, W_regularizer=l1l2(l1=regl1, l2=regl2),init=weight_init))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(64, 3, 3, W_regularizer=l1l2(l1=regl1, l2=regl2),init=weight_init))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Flatten())
model.add(Dense(50,W_regularizer=l1l2(l1=regl1, l2=regl2),init=weight_init))
#model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(output_dim=1))
model.add(Activation('sigmoid'))
model.compile(optimizer=optimize, loss='binary_crossentropy', metrics=['accuracy'])
return model
Example 69
| Project: MODS_ConvNet Author: santiagolopezg File: foo_two.py MIT License | 4 votes |
|
def foo():
# Determine proper input shape
if keras.__version__ > '1.0.3':
K.set_image_dim_ordering('th')
input_shape = (1, 224, 224)
#img_input = Input(shape=input_shape)
model = Sequential()
model.add(Convolution2D(16, 3, 3,
input_shape=input_shape,init=weight_init, name='conv1_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(16, 3, 3,init=weight_init, name='conv1_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(32, 3, 3,init=weight_init, name='conv2_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3,init=weight_init, name='conv2_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3,init=weight_init, name='conv2_3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(64, 3, 3,init=weight_init, name='conv3_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3,init=weight_init, name='conv3_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(128, 3,3,init=weight_init, border_mode='same', name='conv4_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(128, 3,3,init=weight_init, border_mode='same', name='conv4_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Flatten())
model.add(Dense(120, init=weight_init))
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(Dropout(dropout))
model.add(Dense(2))
model.add(Activation('sigmoid'))
return model
Example 70
| Project: MODS_ConvNet Author: santiagolopezg File: foo_three.py MIT License | 4 votes |
|
def foo():
# Determine proper input shape
if keras.__version__ > '1.0.3':
K.set_image_dim_ordering('th')
input_shape = (1, 224, 224)
#img_input = Input(shape=input_shape)
model = Sequential()
model.add(Convolution2D(32, 3, 3,
input_shape=input_shape,init=weight_init, name='conv1_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3,init=weight_init, name='conv1_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(64, 3, 3,init=weight_init, name='conv2_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3,init=weight_init, name='conv2_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3,init=weight_init, name='conv2_3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(128, 3, 3,init=weight_init, name='conv3_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(128, 3, 3,init=weight_init, name='conv3_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Convolution2D(512, 3,3,init=weight_init, border_mode='same', name='conv4_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(512, 3,3,init=weight_init, border_mode='same', name='conv4_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Flatten())
model.add(Dense(120, init=weight_init))
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(Dropout(dropout))
model.add(Dense(2))
model.add(Activation('sigmoid'))
return model
Example 71
| Project: MODS_ConvNet Author: santiagolopezg File: little_foo2.py MIT License | 4 votes |
|
def foo():
# Determine proper input shape
if keras.__version__ > '1.0.3':
K.set_image_dim_ordering('th')
input_shape = (1, 224, 224)
#img_input = Input(shape=input_shape)
model = Sequential()
model.add(Convolution2D(32, 7, 7,
input_shape=input_shape,init=weight_init, name='conv1_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(32, 5, 5,init=weight_init, name='conv1_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3,init=weight_init, name='conv1_3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2))) # in 212, out 106
model.add(Dropout(dropout))
model.add(Convolution2D(64, 7, 7,init=weight_init, name='conv2_1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(64, 5, 5,init=weight_init, name='conv2_2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3,init=weight_init, name='conv2_3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2))) # in is 94, out is 47
model.add(Dropout(dropout))
model.add(Flatten())
model.add(Dense(220, init=weight_init))
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(Dense(2))
model.add(Activation('sigmoid'))
return model
Example 72
| Project: neural-fingerprinting Author: StephanZheng File: utils_keras.py BSD 3-Clause "New" or "Revised" License | 4 votes |
|
def cnn_model(logits=False, input_ph=None, img_rows=28, img_cols=28,
channels=1, nb_filters=64, nb_classes=10):
"""
Defines a CNN model using Keras sequential model
:param logits: If set to False, returns a Keras model, otherwise will also
return logits tensor
:param input_ph: The TensorFlow tensor for the input
(needed if returning logits)
("ph" stands for placeholder but it need not actually be a
placeholder)
:param img_rows: number of row in the image
:param img_cols: number of columns in the image
:param channels: number of color channels (e.g., 1 for MNIST)
:param nb_filters: number of convolutional filters per layer
:param nb_classes: the number of output classes
:return:
"""
model = Sequential()
# Define the layers successively (convolution layers are version dependent)
if keras.backend.image_dim_ordering() == 'th':
input_shape = (channels, img_rows, img_cols)
else:
input_shape = (img_rows, img_cols, channels)
layers = [conv_2d(nb_filters, (8, 8), (2, 2), "same",
input_shape=input_shape),
Activation('relu'),
conv_2d((nb_filters * 2), (6, 6), (2, 2), "valid"),
Activation('relu'),
conv_2d((nb_filters * 2), (5, 5), (1, 1), "valid"),
Activation('relu'),
Flatten(),
Dense(nb_classes)]
for layer in layers:
model.add(layer)
if logits:
logits_tensor = model(input_ph)
model.add(Activation('softmax'))
if logits:
return model, logits_tensor
else:
return model
Example 73
| Project: Scene-Understanding Author: foamliu File: vgg16.py MIT License | 4 votes |
|
def vgg16_model(img_rows, img_cols, channel=3):
model = Sequential()
# Encoder
model.add(ZeroPadding2D((1, 1), input_shape=(img_rows, img_cols, channel), name='input'))
model.add(Conv2D(64, (3, 3), activation='relu', name='conv1_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(64, (3, 3), activation='relu', name='conv1_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(128, (3, 3), activation='relu', name='conv2_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(128, (3, 3), activation='relu', name='conv2_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3), activation='relu', name='conv3_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3), activation='relu', name='conv3_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3), activation='relu', name='conv3_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation='relu', name='conv4_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation='relu', name='conv4_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation='relu', name='conv4_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation='relu', name='conv5_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation='relu', name='conv5_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation='relu', name='conv5_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# Add Fully Connected Layer
model.add(Flatten(name='flatten'))
model.add(Dense(4096, activation='relu', name='dense1'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu', name='dense2'))
model.add(Dropout(0.5))
model.add(Dense(1000, activation='softmax', name='softmax'))
# Loads ImageNet pre-trained data
weights_path = 'models/vgg16_weights_tf_dim_ordering_tf_kernels.h5'
model.load_weights(weights_path)
return model
Example 74
| Project: ANN Author: waynezv File: ANN.py MIT License | 4 votes |
|
def train_mlp(self, input, output):
self.in_real = input.data['real']
self.in_imag = input.data['imag']
self.out_real = output.data['real']
self.out_imag = output.data['imag']
(i_dim_x, i_dim_y, i_dim_z) = self.in_real.shape
in_dim = i_dim_x*i_dim_y*i_dim_z
input_data = self.in_real.reshape(in_dim, 1)
(o_dim_x, o_dim_y, o_dim_z) = self.out_real.shape
out_dim = o_dim_x*o_dim_y*o_dim_z
output_data = self.out_real.reshape(out_dim, 1)
model = Sequential()
model.add(Dense(200, input_dim=in_dim, init='uniform'))
model.add(Activation('relu'))
# model.add(Dropout(0.25))
model.add(Dense(200))#, init='uniform'))
model.add(Activation('relu'))
# model.add(Dropout(0.25))
model.add(Dense(out_dim))#, init='uniform'))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd',\
metrics=['accuracy'])
early_stop = EarlyStopping(monitor='val_loss', patience=2)
hist = model.fit(input_data, output_data, nb_epoch=50, \
batch_size=64, validation_split=0.2, \
shuffle=True, callbacks=[early_stop])
print(hist.history)
#TODO: batch train
model.train_on_batch()
# Save model
model_to_save_json = model.to_json()
open('model_architecture.json', 'w').write(model_to_save_json)
model_to_save_yaml = model.to_yaml()
open('model_architecture.yaml', 'w').write(model_to_save_yaml)
model.save_weights('weights.h5')
Example 75
| Project: ANN Author: waynezv File: ANN_large_v22.py MIT License | 4 votes |
|
def train_mlp(self, input, output):
self.in_real = input.data['real']
self.in_imag = input.data['imag']
self.out_real = output.data['real']
self.out_imag = output.data['imag']
(i_dim_x, i_dim_y, i_dim_z) = self.in_real.shape
in_dim = i_dim_x*i_dim_y*i_dim_z
input_data = self.in_real.reshape(in_dim, 1)
(o_dim_x, o_dim_y, o_dim_z) = self.out_real.shape
out_dim = o_dim_x*o_dim_y*o_dim_z
output_data = self.out_real.reshape(out_dim, 1)
model = Sequential()
model.add(Dense(200, input_dim=in_dim, init='uniform'))
model.add(Activation('relu'))
# model.add(Dropout(0.25))
model.add(Dense(200))#, init='uniform'))
model.add(Activation('relu'))
# model.add(Dropout(0.25))
model.add(Dense(out_dim))#, init='uniform'))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd',\
metrics=['accuracy'])
early_stop = EarlyStopping(monitor='val_loss', patience=2)
hist = model.fit(input_data, output_data, nb_epoch=50, \
batch_size=64, validation_split=0.2, \
shuffle=True, callbacks=[early_stop])
print(hist.history)
#TODO: batch train
model.train_on_batch()
# Save model
model_to_save_json = model.to_json()
open('model_architecture.json', 'w').write(model_to_save_json)
model_to_save_yaml = model.to_yaml()
open('model_architecture.yaml', 'w').write(model_to_save_yaml)
model.save_weights('weights.h5')
Example 76
| Project: ANN Author: waynezv File: ANN_large_v3.py MIT License | 4 votes |
|
def train_mlp(self, input, output):
self.in_real = input.data['real']
self.in_imag = input.data['imag']
self.out_real = output.data['real']
self.out_imag = output.data['imag']
(i_dim_x, i_dim_y, i_dim_z) = self.in_real.shape
in_dim = i_dim_x*i_dim_y*i_dim_z
input_data = self.in_real.reshape(in_dim, 1)
(o_dim_x, o_dim_y, o_dim_z) = self.out_real.shape
out_dim = o_dim_x*o_dim_y*o_dim_z
output_data = self.out_real.reshape(out_dim, 1)
model = Sequential()
model.add(Dense(200, input_dim=in_dim, init='uniform'))
model.add(Activation('relu'))
# model.add(Dropout(0.25))
model.add(Dense(200))#, init='uniform'))
model.add(Activation('relu'))
# model.add(Dropout(0.25))
model.add(Dense(out_dim))#, init='uniform'))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd',\
metrics=['accuracy'])
early_stop = EarlyStopping(monitor='val_loss', patience=2)
hist = model.fit(input_data, output_data, nb_epoch=50, \
batch_size=64, validation_split=0.2, \
shuffle=True, callbacks=[early_stop])
print(hist.history)
#TODO: batch train
model.train_on_batch()
# Save model
model_to_save_json = model.to_json()
open('model_architecture.json', 'w').write(model_to_save_json)
model_to_save_yaml = model.to_yaml()
open('model_architecture.yaml', 'w').write(model_to_save_yaml)
model.save_weights('weights.h5')
Example 77
| Project: ANN Author: waynezv File: ANN_large_v2.py MIT License | 4 votes |
|
def train_mlp(self, input, output):
self.in_real = input.data['real']
self.in_imag = input.data['imag']
self.out_real = output.data['real']
self.out_imag = output.data['imag']
(i_dim_x, i_dim_y, i_dim_z) = self.in_real.shape
in_dim = i_dim_x*i_dim_y*i_dim_z
input_data = self.in_real.reshape(in_dim, 1)
(o_dim_x, o_dim_y, o_dim_z) = self.out_real.shape
out_dim = o_dim_x*o_dim_y*o_dim_z
output_data = self.out_real.reshape(out_dim, 1)
model = Sequential()
model.add(Dense(200, input_dim=in_dim, init='uniform'))
model.add(Activation('relu'))
# model.add(Dropout(0.25))
model.add(Dense(200))#, init='uniform'))
model.add(Activation('relu'))
# model.add(Dropout(0.25))
model.add(Dense(out_dim))#, init='uniform'))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd',\
metrics=['accuracy'])
early_stop = EarlyStopping(monitor='val_loss', patience=2)
hist = model.fit(input_data, output_data, nb_epoch=50, \
batch_size=64, validation_split=0.2, \
shuffle=True, callbacks=[early_stop])
print(hist.history)
#TODO: batch train
model.train_on_batch()
# Save model
model_to_save_json = model.to_json()
open('model_architecture.json', 'w').write(model_to_save_json)
model_to_save_yaml = model.to_yaml()
open('model_architecture.yaml', 'w').write(model_to_save_yaml)
model.save_weights('weights.h5')
Example 78
| Project: phoneticSimilarity Author: ronggong File: models.py GNU Affero General Public License v3.0 | 4 votes |
|
def jan_original(filter_density, dropout, input_shape, batchNorm=False, dense_activation='relu', channel=1):
if channel == 1:
reshape_dim = (1, input_shape[0], input_shape[1])
channel_order = 'channels_first'
else:
reshape_dim = input_shape
channel_order = 'channels_last'
model_1 = Sequential()
if batchNorm:
model_1.add(BatchNormalization(axis=1, input_shape=reshape_dim))
model_1.add(Conv2D(int(10 * filter_density), (3, 7), padding="valid",
input_shape=reshape_dim,
data_format=channel_order, activation='relu'))
model_1.add(MaxPooling2D(pool_size=(3, 1), padding='valid', data_format=channel_order))
model_1.add(Conv2D(int(20 * filter_density), (3, 3), padding="valid",
data_format=channel_order, activation='relu'))
model_1.add(MaxPooling2D(pool_size=(3, 1), padding='valid', data_format=channel_order))
if dropout:
model_1.add(Dropout(dropout)) # test Schluter dataset, comment in jingju dataset
model_1.add(Flatten())
model_1.add(Dense(units=256, activation=dense_activation))
# model_1.add(ELU())
if dropout:
model_1.add(Dropout(dropout))
model_1.add(Dense(1, activation='sigmoid'))
# model_1.add(Activation("softmax"))
# optimizer = SGD(lr=0.05, momentum=0.45, decay=0.0, nesterov=False)
optimizer = Adam()
model_1.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
print(model_1.summary())
return model_1
Example 79
| Project: ndparse Author: neurodata File: nddl.py Apache License 2.0 | 4 votes |
|
def ciresan_n3(n=65, nOutput=2):
"""An approximation of the N3 network from [1].
Note that we also made a few small modifications along the way
(from Theano to caffe and now to tensorflow/keras).
As of this writing, no serious attempt has been made to optimize
hyperparameters or structure of this network.
Parameters:
n : The tile size (diameter) to use in the sliding window.
Tiles are assumed to be square, hence only one parameter.
[1] Ciresan et al 'Deep neural networks segment neuronal membranes in
electron microscopy images,' NIPS 2012.
"""
from keras.optimizers import SGD
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
model = Sequential()
# input: nxn images with 1 channel -> (1, n, n) tensors.
# this applies 48 convolution filters of size 5x5 each.
model.add(Convolution2D(48, 5, 5, border_mode='valid', dim_ordering='th', input_shape=(1, n, n)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2)))
model.add(BatchNormalization()) # note: we used LRN previously...
model.add(Convolution2D(48, 5, 5))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2)))
model.add(BatchNormalization()) # note: we used LRN previously...
#model.add(Dropout(0.25))
model.add(Convolution2D(48, 5, 5, border_mode='valid'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2)))
model.add(Flatten())
# Note: Keras does automatic shape inference.
model.add(Dense(200))
model.add(Activation('relu'))
#model.add(Dropout(0.5))
model.add(Dense(nOutput)) # use 2 for binary classification
model.add(Activation('softmax'))
return model
#-------------------------------------------------------------------------------
# Code for training a deep learning network
#-------------------------------------------------------------------------------
Example 80
| Project: Deep-Learning-for-HSI-classification Author: luozm File: cnn.py MIT License | 4 votes |
|
def cnn_3d(input_shape):
model = Sequential()
model.add(Conv3D(16, kernel_size=(3, 3, 20), strides=(1, 1, 10), padding='valid', kernel_regularizer=l2(REG_lambda), input_shape=input_shape))
# model.add(BatchNormalization())
model.add(Activation(activation='relu'))
model.add(Conv3D(16, kernel_size=(3, 3, 3), strides=(1, 1, 1), padding='same', kernel_regularizer=l2(REG_lambda)))
# model.add(BatchNormalization())
model.add(Activation(activation='relu'))
model.add(MaxPooling3D(pool_size=(2, 2, 3)))
model.add(Conv3D(32, kernel_size=(3, 3, 3), strides=(1, 1, 1), padding='same', kernel_regularizer=l2(REG_lambda)))
# model.add(BatchNormalization())
model.add(Activation(activation='relu'))
model.add(Conv3D(32, kernel_size=(3, 3, 3), strides=(1, 1, 1), padding='same', kernel_regularizer=l2(REG_lambda)))
# model.add(BatchNormalization())
model.add(Activation(activation='relu'))
model.add(MaxPooling3D(pool_size=(2, 2, 3)))
model.add(Conv3D(64, kernel_size=(2, 2, 2), strides=(1, 1, 1), padding='same', kernel_regularizer=l2(REG_lambda)))
# model.add(BatchNormalization())
model.add(Activation(activation='relu'))
model.add(Conv3D(64, kernel_size=(2, 2, 2), strides=(1, 1, 1), padding='same', kernel_regularizer=l2(REG_lambda)))
# model.add(BatchNormalization())
model.add(Activation(activation='relu'))
model.add(MaxPooling3D(pool_size=(2, 2, 2)))
model.add(Flatten())
model.add(Dense(128))
# model.add(BatchNormalization())
model.add(Activation(activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes, activation='softmax'))
adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=['accuracy'])
return model
# 2D-CNN model
