print(pred_1)
eval_1 = model_1.evaluate(x_test, y_test)
print(eval_1)
model_1.summary()
#plotting the val_acc
plt.plot(training.history['val_acc'], 'b', training_1.history['val_acc'], 'r')
plt.xlabel('Epochs')
plt.ylabel('Validation score')
plt.show()
#Creating a TSNE model
model_1 = TSNE(learning_rate=150, perplexity=30)
transformed = model_1.fit_transform(corona)
xs = transformed[:, 0]
ys = transformed[:, 1]
#Labelling
plt.scatter(xs, ys, alpha=0.5)
for a, b, c_virus in zip(xs, ys, samples):
plt.annotate(c_virus, (a, b), fontsize=5, alpha=0.75)
plt.show()
plt.show()
#Illustrate the feedforward network model
plot_model(model, to_file='corona_model.png')
data = plt.imread('corona_model.png')
plt.imshow(data)
plt.show()
model.add(Dense(8, activation='elu'))
model.add(Dense(10, activation='elu'))
model.add(Dense(X.shape[1], activation='sigmoid'))
model.compile(loss='mean_squared_error', optimizer=Adam())
model.fit(X, X, batch_size=128, epochs=100, shuffle=True, verbose=1)
encoder = Model(model.input, model.get_layer('bottleneck').output)
bottleneck_representation = encoder.predict(X)
model_tsne_auto = TSNE(learning_rate=200,
n_components=2,
random_state=123,
perplexity=90,
n_iter=1000,
verbose=1)
tsne_auto = model_tsne_auto.fit_transform(bottleneck_representation)
plt.scatter(tsne_auto[:, 0], tsne_auto[:, 1], c=Y, cmap='tab20', s=10)
plt.title('tSNE on Autoencoder: 8 Layers')
plt.xlabel("tSNE1")
plt.ylabel("tSNE2")
from umap import UMAP
model = UMAP(n_neighbors=30, min_dist=0.3, n_components=2)
umap = model.fit_transform(X_reduced)
umap_coords = pd.DataFrame({'UMAP1': umap[:, 0], 'UMAP2': umap[:, 1]})
umap_coords.to_csv('umap_coords_10X_1.3M_MouseBrain.txt', sep='\t')
plt.scatter(umap[:, 0], umap[:, 1], c=Y, cmap='tab20', s=1)
plt.title('UMAP')
plt.xlabel("UMAP1")
plt.ylabel("UMAP2")
print(newProba[11])
print(newProba[21])
print("1")
print(newProba[0:9])
print("2")
print(newProba[10:19])
print("3")
print(newProba[20:-1])
print(labels)
print(model.predict_classes(X))
from sklearn.manifold import TSNE
model = TSNE(n_components=nb_classes, random_state=0, init="pca")
toPlot = model.fit_transform(newProba)
title = "t-SNE embedding of the spectrograms"
x_min, x_max = np.min(toPlot, 0), np.max(toPlot, 0)
toPlot = (toPlot - x_min) / (x_max - x_min)
print(toPlot.shape)
labelsName = ["bob", "steve", "dave"]
cmap = sns.color_palette("Set2", n_colors=3)
plt.figure()
for i in range(toPlot.shape[0]):
plt.text(
from keras.layers import Dense
from keras.applications import MobileNetV2
datagen = ImageDataGenerator(rescale=1./255,shear_range=0.2,zoom_range=0.3)
train_generator = datagen.flow_from_directory('data/dataset/train',
target_size=(224, 224),
batch_size=64,
class_mode='categorical')
test_generator = datagen.flow_from_directory('data/dataset/test',
target_size=(224, 224),
batch_size=64,
class_mode='categorical')
mobile = MobileNetV2(include_top=False,
weights="imagenet",
input_shape=(224,224,3),
pooling="avg")
model = Sequential()
model.add(mobile)
model.add(Dense(5, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit_transform(train_generator,
epochs=10,
steps_per_epoch=2360/64,
validation_data=test_generator,
validation_steps=263/64)
model.save('data/model.h5')
print(newProba[2])
print(newProba[11])
print(newProba[21])
print("1")
print(newProba[0:9])
print("2")
print(newProba[10:19])
print("3")
print(newProba[20:-1])
print(labels)
print(model.predict_classes(X))
from sklearn.manifold import TSNE
model = TSNE(n_components=nb_classes, random_state=0, init='pca')
toPlot = model.fit_transform(newProba)
title = "t-SNE embedding of the spectrograms"
x_min, x_max = np.min(toPlot, 0), np.max(toPlot, 0)
toPlot = (toPlot - x_min) / (x_max - x_min)
print(toPlot.shape)
labelsName = ["bob", "steve", "dave"]
cmap = sns.color_palette("Set2", n_colors=3)
plt.figure()
for i in range(toPlot.shape[0]):
plt.text(toPlot[i, 0],
toPlot[i, 1],
