zabop

import shapely.geometry
import shapely.affinity
import geopandas as gpd

origin = shapely.geometry.Point(67596.000000, 36694.000000)
pixel_count = 256
units_per_pixel_for_each_zoom_level = [
    352.77758727788068426889,
    176.38879363894034213445,
    88.19439681947017106722,

zabop

zabop

zabop

dfs = pd.read_html('https://en.wikipedia.org/wiki/Doppler_spectroscopy')

zabop

import pandas as pd
df = pd.read_html('https://en.wikipedia.org/wiki/List_of_cities_in_India_by_population')[0]

zabop

plt.figure(figsize=(8,6))
plt.plot(epochs, history_dict1['val_acc'], 'b', linewidth=5, label='Accuracy, ReLU, 16 neurons')
plt.plot(epochs, history_dict2['val_acc'], 'r', linewidth=5, label='Accuracy, Sigmoid, 16 neurons')
plt.plot(epochs, history_dict3['val_acc'], 'c', linewidth=5, label='Accuracy, ReLU, 64 neurons')
plt.plot(epochs, history_dict4['val_acc'], 'k', linewidth=5, label='Accuracy, Sigmoid, 64 neurons')

plt.title('Accuracy vs epochs',fontsize=22)
plt.xlabel('Epochs',fontsize=18)
plt.ylabel('Accuracy',fontsize=18)

zabop

plt.figure(figsize=(8,6))

epochs = range(1, 21)

plt.plot(epochs, val_loss_values1, 'b', linewidth=5, label='Validation loss, ReLU, 16 neurons')
plt.plot(epochs, val_loss_values2, 'r', linewidth=5, label='Validation loss, Sigmoid, 16 neurons')
plt.plot(epochs, val_loss_values3, 'c', linewidth=5, label='Validation loss, ReLU, 64 neurons')
plt.plot(epochs, val_loss_values4, 'k', linewidth=5, label='Validation loss, Sigmoid, 64 neurons')

plt.title('Training and validation loss',fontsize=22)

zabop

history_dict1 = history1.history
loss_values1 = history_dict1['loss']
val_loss_values1 = history_dict1['val_loss']

zabop

model1 = models.Sequential()
model1.add(layers.Dense(16, activation='relu', input_shape=(10000,)))
model1.add(layers.Dense(16, activation='relu'))
model1.add(layers.Dense(1, activation='sigmoid'))

model1.compile(optimizer='rmsprop',
                loss='binary_crossentropy',
                metrics=['acc'])

history1 = model1.fit(partial_x_train,

zabop

dimension=10000
mlb = MultiLabelBinarizer(classes=range(dimension))

x_train = mlb.fit_transform(train_data)
x_test  =  mlb.fit_transform(test_data)

y_train = np.asarray(train_labels).astype('float32')
y_test = np.asarray(test_labels).astype('float32')

x_val = x_train[:10000]