def test_add_method():
model = Sequential()
model.add(Dense(n_nodes=32, n_inputs=45))
model.build()
with pytest.raises(Exception):
model.add(Dense(n_nodes=32, n_inputs=45))
from neuralpy.models import Sequential
from neuralpy.layers.convolutional import Conv2D
from neuralpy.layers.linear import Dense
from neuralpy.layers.other import Flatten
from neuralpy.layers.activation_functions import ReLU, Softmax
from neuralpy.loss_functions import CrossEntropyLoss
from neuralpy.optimizer import SGD
import torch
import torchvision
from torchvision import datasets, transforms
# Create a Sequential model Instance
model = Sequential()
#Build your network
model.add(Conv2D(input_shape=(1, 28, 28), filters=128, kernel_size=3))
model.add(ReLU())
model.add(Conv2D(filters=64, kernel_size=3))
model.add(ReLU())
model.add(Conv2D(filters=32, kernel_size=3))
model.add(ReLU())
model.add(Flatten())
model.add(Dense(n_nodes=10))
model.build()
model.compile(optimizer=SGD(),
loss_function=CrossEntropyLoss(),
metrics=["accuracy"])
print(model.summary())
#Get the MNIST dataset
# Random seed for numpy np.random.seed(1969) # Generating the data X_train = np.random.rand(100, 1) * 10 y_train = X_train + 5 *np.random.rand(100, 1) X_validation = np.random.rand(100, 1) * 10 y_validation = X_validation + 5 * np.random.rand(100, 1) X_test = np.random.rand(10, 1) * 10 y_test = X_test + 5 * np.random.rand(10, 1) # Making the model model = Sequential() model.add(Dense(n_nodes=1, n_inputs=1)) # Building the model model.build() # Compiling the model model.compile(optimizer=Adam(), loss_function=MSELoss()) # Printing model summary model.summary() # Training the model history = model.fit(train_data=(X_train, y_train), validation_data=(X_validation, y_validation), epochs=300, batch_size=4) # Predicting some values
# Dependencies
from neuralpy.models import Sequential
from neuralpy.layers import Dense
from neuralpy.regularizers import Dropout
from neuralpy.activation_functions import ReLU
from neuralpy.loss_functions import CrossEntropyLoss
from neuralpy.optimizer import Adam
import pandas as pd
import numpy as np
# Model
model = Sequential()
model.add(Dense(n_nodes=64, n_inputs=784))
model.add(ReLU())
model.add(Dropout())
model.add(Dense(n_nodes=10))
model.build()
model.compile(optimizer=Adam(learning_rate=0.001,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=0.0,
amsgrad=False),
loss_function=CrossEntropyLoss(),
metrics=["accuracy"])
import pytest
import numpy as np
np.random.seed(1969)
X_train = np.random.rand(100, 1) * 10
y_train = X_train + 5 * np.random.rand(100, 1)
X_validation = np.random.rand(100, 1) * 10
y_validation = X_validation + 5 * np.random.rand(100, 1)
X_test = np.random.rand(10, 1) * 10
y_test = X_test + 5 * np.random.rand(10, 1)
model = Sequential()
model.add(Dense(n_nodes=1, n_inputs=1))
model.build()
pytorch_model = model.get_model()
def train_generator():
for i in range(40):
X_train = np.random.rand(40, 1) * 10
y_train = X_train + 5 * np.random.rand(40, 1)
yield X_train, y_train
from neuralpy.models import Sequential from neuralpy.layers.linear import Dense from neuralpy.layers.convolutional import Conv2D from neuralpy.layers.activation_functions import ReLU,Softmax from neuralpy.layers.pooling import MaxPool2D from neuralpy.layers.other import Flatten from neuralpy.loss_functions import CrossEntropyLoss from neuralpy.optimizer import SGD,Adam import torch import torchvision.datasets as datasets import torchvision.transforms as transforms # Create a Sequential model Instance model = Sequential() model.add(Conv2D(input_shape=(1,224,224), filters=96, kernel_size=11, stride=4)) model.add(ReLU()) model.add(MaxPool2D(kernel_size=3, stride=2)) model.add(ReLU()) model.add(Conv2D(filters=256, kernel_size=5, stride=1, padding=2)) model.add(ReLU()) model.add(MaxPool2D(kernel_size=3, stride=2)) model.add(ReLU()) model.add(Conv2D(filters=384, kernel_size=3, stride=1, padding=1)) model.add(ReLU()) model.add(Conv2D(filters=384, kernel_size=3, stride=1, padding=1)) model.add(ReLU()) model.add(Conv2D(filters=256, kernel_size=3, stride=1, padding=1)) model.add(ReLU()) model.add(MaxPool2D(kernel_size=3, stride=2))
# Dependencies
from neuralpy.models import Sequential
from neuralpy.layers.linear import Dense
from neuralpy.layers.regularizers import Dropout
from neuralpy.layers.activation_functions import ReLU
from neuralpy.loss_functions import CrossEntropyLoss
from neuralpy.optimizer import Adam
import pandas as pd
import numpy as np
# Model
model = Sequential()
model.add(Dense(n_nodes=264, n_inputs=784))
model.add(ReLU())
model.add(Dropout())
model.add(Dense(n_nodes=10))
model.build()
model.compile(optimizer=Adam(),
loss_function=CrossEntropyLoss(),
metrics=["accuracy"])
print(model.summary())
# Reading data
train_data = pd.read_csv("./data/mnist_train.csv", header=None)
from neuralpy.models import Sequential from neuralpy.layers.linear import Dense from neuralpy.layers.activation_functions import ReLU from neuralpy.loss_functions import MSELoss from neuralpy.optimizer import Adam import numpy as np # Creating Model ''' This example will create an ann(Artifical Neural Network) for a 3 input XOR logic ''' model = Sequential() model.add(Dense(n_nodes=1, n_inputs=3)) model.add(ReLU()) model.add(Dense(n_nodes=2)) model.add(ReLU()) model.add(Dense(n_nodes=1)) model.add(ReLU()) # Building the Model model.build() # Compiling model.compile(optimizer=Adam(), loss_function=MSELoss(), metrics=["accuracy"]) print(model.summary()) # Data for XOR