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df.columns = pd.MultiIndex.from_product([['Emergency'], ['全体', 'D+E', 'C', 'B', 'A']])

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dic = {('A', 'a'): [1, 2, 3, 4, 5],
 ('A', 'b'): [6, 7, 8, 9, 1],
 ('B', 'a'): [2, 3, 4, 5, 6],
 ('B', 'b'): [7, 8, 9, 1, 2]}

pd.DataFrame(dic)

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for i in range(ord('a'), ord('z')+1):
    print(chr(i))

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import keras.backend as K
K.set_image_data_format('channels_last')

def Model(input_shape):
    # Define the input placeholder as a tensor with shape input_shape. Think of this as your input image!
    X_input = Input(input_shape)

    # Zero-Padding: pads the border of X_input with zeroes
    X = ZeroPadding2D((3, 3))(X_input)

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from pymongo import MongoClient
db_setting = ["user_name","password","ip_address","db_name"]
client = MongoClient("mongodb://{}:{}@{}/{}?authMechanism=SCRAM-SHA-1".format(*db_setting))
db = client["db_name"]

db['COLLECTION_NAME'].find().count()

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X, Y = create_placeholders(n_H0, n_W0, n_C0, n_y)
parameters = initialize_parameters()
Z3 = forward_propagation(X, parameters)

with tf.Session() as sess:
        
    # Run the initialization
    sess.run(init)
    
    

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try:
    import httplib
except:
    import http.client as httplib

def have_internet():
    conn = httplib.HTTPConnection("www.google.com", timeout=5)
    try:
        conn.request("HEAD", "/")
        conn.close()

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conda install -c conda-forge selenium
conda install -c conda-forge imageio


References:
- https://anaconda.org/conda-forge/selenium
- https://stackoverflow.com/questions/46137219/how-to-install-selenium-in-a-conda-environment

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1.2 - Forward propagation

In TensorFlow, there are built-in functions that carry out the convolution steps for you.

• tf.nn.conv2d(X,W1, strides = [1,s,s,1], padding = 'SAME'): given an input $X$ and a group of filters $W1$, this function convolves $W1$'s filters on X. The third input ([1,f,f,1]) represents the strides for each dimension of the input (m, n_H_prev, n_W_prev, n_C_prev). You can read the full documentation here

• tf.nn.max_pool(A, ksize = [1,f,f,1], strides = [1,s,s,1], padding = 'SAME'): given an input A, this function uses a window of size (f, f) and strides of size (s, s) to carry out max pooling over each window. You can read the full documentation here

• tf.nn.relu(Z1): computes the elementwise ReLU of Z1 (which can be any shape). You can read the full documentation here.

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pip install pillow

References
- https://github.com/rouseguy/DeepLearning-Image/issues/1